diff --git a/README.md b/README.md
index fa4b356..131381a 100644
--- a/README.md
+++ b/README.md
@@ -2,14 +2,14 @@
 
 # ECNet: scalable, retrainable and deployable machine learning projects for fuel property prediction
 
-[![GitHub version](https://badge.fury.io/gh/tjkessler%2FECNet.svg)](https://badge.fury.io/gh/tjkessler%2FECNet)
+[![GitHub version](https://badge.fury.io/gh/ecrl%2FECNet.svg)](https://badge.fury.io/gh/ecrl%2FECNet)
 [![PyPI version](https://badge.fury.io/py/ecnet.svg)](https://badge.fury.io/py/ecnet)
 [![status](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f/status.svg)](http://joss.theoj.org/papers/f556afbc97e18e1c1294d98e0f7ff99f)
-[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/TJKessler/ECNet/master/LICENSE.txt)
+[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/ECRL/ECNet/master/LICENSE.txt)
 [![Documentation Status](https://readthedocs.org/projects/ecnet/badge/?version=latest)](https://ecnet.readthedocs.io/en/latest/?badge=latest)
 [![Build Status](https://dev.azure.com/uml-ecrl/package-management/_apis/build/status/ECRL.ECNet?branchName=master)](https://dev.azure.com/uml-ecrl/package-management/_build/latest?definitionId=1&branchName=master)
 	
-**ECNet** is an open source Python package for creating scalable, retrainable and deployable machine learning projects with a focus on fuel property prediction. An ECNet __project__ is considered a collection of __pools__, where each pool contains a neural network that has been selected from a group of __candidate__ neural networks. Candidates are selected to represent pools based on their ability to optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes a prediction derived from input data, and these predictions are averaged to calculate the project's final prediction. Using multiple pools allows a project to learn from a variety of learning and validation sets, which can reduce the project's prediction error. Projects can be saved and reused at a later time allowing additional training and deployable predictive models. 
+**ECNet** is an open source Python package for creating scalable, retrainable and deployable machine learning projects with a focus on fuel property prediction. An ECNet __project__ is considered a collection of __pools__, where each pool contains a neural network that has been selected from a group of __candidate__ neural networks. Candidates are selected to represent pools based on their ability to optimize certain learning criteria (for example, performing optimially on unseen data). Each pool contributes a prediction derived from input data, and these predictions are averaged to calculate the project's final prediction. Using multiple pools allows a project to learn from a variety of learning and validation sets, which can reduce the project's prediction error. Projects can be saved and reused at a later time allowing additional training and deployable predictive models.
 
 [T. Sennott et al.](https://doi.org/10.1115/ICEF2013-19185) have shown that neural networks can be applied to cetane number prediction with relatively little error. ECNet provides scientists an open source tool for predicting key fuel properties of potential next-generation biofuels, reducing the need for costly fuel synthesis and experimentation.
 
@@ -17,7 +17,7 @@
   <img align="center" src="docs/img/workflow_diagram.png" width="50%" height="50%">
 </p>
 
-Using ECNet, [T. Kessler et al.](https://doi.org/10.1016/j.fuel.2017.06.015) have increased the generalizability of neural networks to predict the cetane number for a variety of molecular classes represented in our [cetane number database](https://github.com/TJKessler/ECNet/tree/master/databases), and have increased the accuracy of neural networks for predicting the cetane number of underrepresented molecular classes through targeted database expansion.
+Using ECNet, [T. Kessler et al.](https://doi.org/10.1016/j.fuel.2017.06.015) have increased the generalizability of neural networks to predict the cetane number for a variety of molecular classes represented in our [cetane number database](https://github.com/ECRL/ECNet/tree/master/databases), and have increased the accuracy of neural networks for predicting the cetane number of underrepresented molecular classes through targeted database expansion.
 
 Future plans for ECNet include:
 - distributed candidate training for GPU's
@@ -34,4 +34,4 @@ To contribute to ECNet, make a pull request. Contributions should include tests
 
 To report problems with the software or feature requests, file an issue. When reporting problems, include information such as error messages, your OS/environment and Python version.
 
-For additional support/questions, contact Travis Kessler (travis.j.kessler@gmail.com), Hernan Gelaf-Romer (hernan_gelafromer@student.uml.edu) and/or John Hunter Mack (Hunter_Mack@uml.edu).
+For additional support/questions, contact Travis Kessler (Travis_Kessler@student.uml.edu) and/or John Hunter Mack (Hunter_Mack@uml.edu).
diff --git a/databases/ysi_database_v2.0.csv b/databases/ysi_database_v2.0.csv
new file mode 100644
index 0000000..fe5a9ab
--- /dev/null
+++ b/databases/ysi_database_v2.0.csv
@@ -0,0 +1,568 @@
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+UMLYSI0118,L,methylcyclopentadiene dimer,CC1=CC2C3CC(C=C3C)C2C1,aromatic,C12H16,26472-00-4,n/a,UML,"2,5-Dimethyl-3a,4,7,7a-tetrahydro-1H-4,7-methanoindene",189.2,160.28,5.724285714,16.2144,27.0688,18.0912,31.3216,0.579085714,0.966742857,0.646114286,1.118628571,0.212121212,28,12,2,30,14,2,16,0,0,2,0,0,16,12,0,0,0,0,0,0,0,0,0,0,0,0,57.14285714,42.85714286,0,0,0,8,4,0,0,1,0,3,6,15,9,3,0.833333333,0.6,1.5,1,0.333333333,0,0,3,1,0,1,1,0,0,0,0,0,0,0,50.86746032,20.31468254,0,27.35595238,31.84166667,0,0,0,72,96,96,72,147.9,100.89537,90,115,115,90,181.42,122.127534,6,5.5,2.444444444,1.930555556,15,22,1.732050808,9.364262454,2,2.182247272,0.326785826,24,6,1.095212214,15,39.92749394,2.865679887,5.04396036,1,56,4.666666667,0.722222222,3.944444444,22,76,18,1.784159128,6,2.575757576,10,7.818181818,842,981,823,1094,11.48837398,125,2005,6.939271861,2.041538241,0.769135656,1.180565739,0.612202381,0.369022236,0.217957935,0.115229759,0.405138889,0.547453704,3.429166667,11.50008972,19.66666667,1.638888889,9.47471524,9.09023137,0.0547249,0.00140602,0.000971876,0.000348592,7.138888889,4.503609527,2.012098496,7.72E-06,0.005747693,0.002252721,0,5,0.729573959,2.708050201,4.290459441,5.198497031,6.142037406,7.072421901,8.017307508,8.954027759,9.897922094,10.83693034,11.78000934,3.36729583,0,4.762173935,3.433987204,6.343880434,5.860786223,8.026496939,8.002694162,9.771611931,2.708050201,3.135494216,3.496507561,3.891820298,4.043051268,4.094344562,4.043051268,3.850147602,3.258096538,1.609437912,2.833213344,3.36729583,3.784189634,4.276666119,4.532599493,4.744932128,4.890349128,4.912654886,4.418840608,2.708050201,12.27399761,5.926926026,6.606650186,1.114684097,0.67972416,3.256634863,2.607626977,8.29252874,5.754020195,5.776908128,5.034805569,4.686194486,3.564336147,0.691044062,0.411001443,0.262586733,0.157337674,0.097629052,0.06364886,7.878315178,5.110990332,4.875081908,4.131526387,3.646898872,2.612887827,0.656526265,0.365070738,0.221594632,0.1291102,0.07597706,0.046658711,8.29252874,5.754020195,5.776908128,5.034805569,4.686194486,3.564336147,34.52412117,2.436404985,81.10538611,5.110990332,5.754020195,4.037057091,4.875608856,134.6059378,27.58638781,0.985228136,2.254200348,5.874795113,3.022055209,3.559929884,148.7772229,998.7151692,1.459147917,3.502736172,12,1,43.01955001,2.306127543,1.462066091,14.7857466,0.40687982,0.560974582,1.363524187,0.985228136,2.448145765,3.726474118,4.039148672,4.324862958,4.324862958,27.58638781,68.54808141,104.3412753,113.0961628,121.0961628,121.0961628,0.204941834,0.509250056,0.775161014,0.840201886,0.899634628,0.899634628,3.822126786,2.359209158,1.080880804,0.76820625,0.482491964,0.482491964,0.197045627,0.489629153,0.745294824,0.807829734,0.864972592,0.864972592,20.13907068,7.904951107,0.658745926,1.077479026,2.563947676,0.213662306,4.81114526,15.80990221,1.317491851,6.135564891,3.497460064,2.918288281,0.24319069,0.558153829,0.127969956,0.010664163,-1.071470377,13.4750957,1.122924641,1.35543823,170,2.575757576,28.33333333,33.41666667,0.531458766,0.03796134,1.860105682,5.877735782,29.23358554,2.436132128,1.759058503,29.23358554,2.436132128,42.39094894,58.46717108,4.872264257,13.67796082,29.23358554,6.989335266,10.01917951,13.54313081,16.85764448,20.26024054,3.116114728,0.259676227,0.592336127,0.062323765,0.005193647,-1.446415583,13.59672208,1.133060174,1.360121065,107.49,14.39222083,1.203586906,4.605170186,28,2.333333333,1.736589638,5.083250939,0.423604245,4.806300989,17.61263494,1.467719578,12.64898446,6.07260843,4.615120517,6.023447593,7.502738211,9.016634256,10.54998852,3.464101615,0.288675135,0.647508748,3.464101615,0.288675135,0.647508748,13.45076492,1.120897077,1.354496379,0.959003366,35.06001956,13.96542847,0.997530605,48.87899964,1.629090003,3.383114068,0.281926172,0.635179969,1,0.083333333,1.874967772,6.766228136,0.563852345,2.044235393,2.746435327,1.773067336,0,1.454875625,0.220671362,1.257064891,3.151439665,0.262619972,0.598210796,0.33483605,0.027903004,-0.570201348,13.49392576,1.124493814,1.35616598,21.95694444,0.332680976,3.659490741,3.675308501,0.262522036,12.86357975,2.984698057,7.655441343,0.637953445,1.06076428,3.86189739,0.321824783,5.673078224,15.31088269,1.27590689,5.862169875,4.16043198,3.472221888,3.894460571,5.588328165,6.731762271,8.146588693,2.902137219,0.241844768,0.555261531,0.079081606,0.006590134,-1.322309422,13.47351607,1.122793005,1.355377133,516,7.818181818,86,10.16865079,0.165938137,0.011852724,0.58078348,7.76937861,86.3358262,7.194652183,2.323429259,86.3358262,7.194652183,104.5132983,172.6716524,14.38930437,25.89484865,86.3358262,9.03990786,13.35434298,17.83639789,22.29060219,26.74958554,2.523760672,0.210313389,0.482457802,0.061671074,0.005139256,-1.451902188,13.72000927,1.143334106,1.364825286,22.73174603,0.344420394,3.788624339,247.5,3.945809277,0.28184352,13.81033247,2.85098018,4.593348509,0.382779042,0.794555277,3.954016713,0.329501393,5.032354354,9.186697018,0.765558085,3.891150412,4.123346924,3.03280882,4.079657969,5.518234091,6.870789381,8.249661734,2.962305109,0.246858759,0.565955745,0.20934643,0.0174455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+UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below)


+


+


+


+cis: C1C[C@H]2CCCC[C@H]2CC1


+


+


+


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alkanone,C9H18O,815-24-7,13152,PubChem,,65.8,142.27,5.081071429,14.456,27.2797,16.3071,31.9462,0.516285714,0.974275,0.582396429,1.140935714,0.2,28,10,7,27,9,1,10,2,0.074074074,1,0,0,18,9,0,1,0,0,0,0,0,0,0,0,1,0,64.28571429,32.14285714,0,3.571428571,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,48,90,101.7355372,50.1037111,126.06,101.5036163,51,80,83.63636364,53.99000833,124.8,92.5890368,7.833333333,6.916666667,2,1.666666667,7,15,3.872983346,3.871201011,1.276595745,1.472733358,0.508249709,21,5,1.234708048,12,30.82322605,2.670830083,7.654493014,1,35,3.5,0.6,3.52,15,72,10,1.295461844,4,2.466666667,4,2.466666667,405,564,273,484,9.555341511,57,111,9.675324675,2.373033188,2.884881456,2.295986656,0.65,0.295238095,0.085714286,0,1.446180556,4.486111111,8.972222222,33.01810606,23.16666667,2.316666667,7.184056211,5.47314386,0.028856616,0.006897229,-0.02221154,0.003392634,7.816666667,3.722515789,1.414509605,4.56969E-05,0.019518469,-0.045359878,9.06E-06,54,1.156779649,2.302585093,3.891820298,4.634728988,5.556828062,6.326149473,7.247792582,8.02059915,8.942591637,9.715892113,10.63799258,2.944438979,0,4.369447852,0,5.937536205,0,7.563719668,0,9.220191688,2.302585093,2.772588722,2.564949357,2.302585093,0,0,0,0,0,0,2.397895273,2.890371758,2.944438979,2.302585093,0,0,0,0,0,0,11.17532459,4.025351691,4.17438727,1.037024238,0.149035579,2.937832606,2.443680008,8.577350269,4.154700538,5.453738644,2.598076211,1.299038106,0,0.857735027,0.461633393,0.363582576,0.216506351,0.144337567,0,7.90824829,3.704124145,4.829124145,1.362372436,1.125,0,0.790824829,0.411569349,0.32194161,0.113531036,0.125,0,8.577350269,4.154700538,5.453738644,2.598076211,1.299038106,0,25.5055535,1.775162974,45.98505863,3.690221478,4.095627583,2.906141204,3.224729682,134.6059378,31.01796278,1.107784385,1.96559623,5.361455055,1.570950594,3.298030961,88.45183037,595.1215111,1.156779649,3.016875696,4,0.4,15.70950594,1.958499469,1.402025991,26.55266679,1.039577419,1.386472124,3.681549487,1.107784385,1.501339742,1.501339742,1.501339742,1.329648495,0.886003325,31.01796278,42.03751279,42.03751279,42.03751279,37.23015787,24.8080931,0.230435323,0.312300582,0.312300582,0.312300582,0.276586297,0.184301625,3.699570537,3.30601518,3.30601518,3.30601518,3.477706427,3.921351597,0.230435323,0.312300582,0.312300582,0.312300582,0.276586297,0.184301625,37.39230485,4.80842881,0.480842881,0.682003191,2.334414218,0.233441422,4.668828437,9.61685762,0.961685762,3.713572067,3.170763419,2.891951981,0.289195198,0.461191077,0.557537762,0.055753776,-0.253725712,8.475616112,0.847561611,0.928171278,111,2.466666667,22.2,22.75,0.470125798,0.0522362,4.231132186,5.379897354,22.84770066,2.284770066,1.3588425,22.84770066,2.284770066,32.43027636,45.69540133,4.569540133,12.76546841,22.84770066,6.466144724,9.303921786,12.54649066,15.63111366,18.77854053,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,111,-10.1581629,1.916515139,0,18,1.8,1.255272505,5.528917957,0.552891796,5.320205805,15.50551323,1.550551323,8.432288684,6.004665238,4.204692619,5.726847748,7.333676396,8.971956271,10.62582939,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.830940108,20.66025404,8.960938896,0.995659877,80.64845006,1.405447334,2.366025404,0.23660254,0.374019403,1,0.1,2,4.732050808,0.473205081,1.681758574,2.575621127,1.609437912,0,1.446918983,0,1.354545663,3.000973155,0.300097315,0.47726211,0.643950551,0.064395055,-0.191147481,8.47871515,0.847871515,0.928330045,14.64583333,0.325462963,2.929166667,2.606174118,0.289574902,23.45556706,2.594009071,5.257194925,0.525719492,0.72075408,3.280925887,0.328092589,5.129189492,10.51438985,1.051438985,4.311832836,3.63502264,3.055961937,3.394010157,4.950141615,5.888801823,7.184028701,2.794741249,0.279474125,0.446341605,0.374073442,0.037407344,-0.427043124,8.482703211,0.848270321,0.928534272,111,2.466666667,22.2,22.75,0.470125798,0.0522362,4.231132186,5.379897354,22.84770066,2.284770066,1.3588425,22.84770066,2.284770066,32.43027636,45.69540133,4.569540133,12.76546841,22.84770066,6.466144724,9.303921786,12.54649066,15.63111366,18.77854053,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,105.625,1.920454545,21.1,25.64752791,0.49350496,0.054833884,4.441544644,5.306913129,43.38258141,21.8162907,2.18162907,1.338780912,21.8162907,2.18162907,31.3988664,43.58258141,4.358258141,12.15267162,21.81629071,0.223143551,6.384509088,9.154096935,12.36766863,15.396746,18.50310326,2.815656588,0.281565659,0.449579685,0,0,0,8.697235167,0.869723517,0.939381213,105.6271,1.920492727,21.10048,25.64415992,0.493493409,0.054832601,4.441440682,5.306943433,43.38401534,21.81670767,2.181670767,1.338789212,21.81670767,2.181670767,31.39928336,43.58353534,4.358353534,12.15327559,21.81670767,0.222663436,6.384543769,9.154159109,12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alkanone,C9H18O,821-55-6,13187,PubChem,,45.1,142.27,5.081071429,14.456,27.2797,16.3071,31.9462,0.516285714,0.974275,0.582396429,1.140935714,0.2,28,10,3,27,9,1,10,6,0.222222222,1,0,0,18,9,0,1,0,0,0,0,0,0,0,0,1,0,64.28571429,32.14285714,0,3.571428571,0,8,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,78,89.73553719,38.1037111,103.46,85.509344,34,58,61.63636364,35.32889259,82.76,62.91136,6.333333333,5.416666667,2.583333333,2.166666667,1,9,3,5.257495372,1.578947368,1.691726385,0.436124566,21,1,1.421594772,7,34.31757639,4.07703596,5.939658627,1,64,6.4,1.2,5.92,24,76,20,2.246439345,8,3.511111111,8,3.511111111,576,820,461,852,10.88209949,111,158,9.675324675,6.790594136,8.825130123,6.570120301,0.501666667,0.219209957,0.093805067,0.046160268,1.336575491,3.476438571,7.35163407,22.33791838,21.66666667,2.166666667,7.46575419,5.383631714,0.042459057,0.006130567,-0.022182193,0.003418219,7.666666667,3.722939441,1.309435081,4.71537E-05,0.019457175,-0.046452233,6.85E-06,22,2.246439345,2.302585093,3.610917913,4.234106505,4.919980926,5.564520407,6.255750042,6.908754779,7.601402335,8.258163362,8.951180966,2.944438979,0,4.007333185,0,5.198497031,0,6.447305863,0,7.727094485,2.302585093,2.302585093,2.079441542,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0,0,2.397895273,2.48490665,2.197224577,2.079441542,1.945910149,1.791759469,1.609437912,1.386294361,0,0,9.655923227,4.025351691,4.17438727,1.037024238,0.149035579,2.97872113,2.426530506,7.819990956,4.77005561,3.596735547,1.885027805,1.156139248,0.692513903,0.781999096,0.530006179,0.399637283,0.269289686,0.192689875,0.138502781,7.150888978,4.264784317,2.866228665,1.632392159,0.97749887,0.566196079,0.715088898,0.473864924,0.318469852,0.23319888,0.162916478,0.113239216,7.819990956,4.77005561,3.596735547,1.885027805,1.156139248,0.692513903,29.19768393,2.219823261,36.85517875,4.250881649,4.690097211,3.518417025,4.056418566,134.6059378,31.01796278,1.107784385,2.856643375,5.238426519,2.521928095,3.290839978,128.5489519,827.6713899,1.295461844,3.239097918,10,1,33.21928095,2.771732595,1.37310439,27.5152295,0.586040345,0.940536678,1.48881419,1.107784385,1.712058833,3.040071227,3.69951385,4.039148672,4.039148672,31.01796278,47.93764732,85.12199436,103.5863878,113.0961628,113.0961628,0.230435323,0.356133229,0.632379193,0.769552885,0.840201886,0.840201886,3.699570537,3.095296089,1.767283695,1.107841072,0.76820625,0.76820625,0.230435323,0.356133229,0.632379193,0.769552885,0.840201886,0.840201886,42.93050049,5.67128182,0.567128182,0.753681229,1.969615506,0.196961551,3.939231012,11.34256364,1.134256364,4.343805422,3.114238058,2.929791425,0.292979142,0.466836704,0.256323536,0.025632354,-0.591211514,8.553825136,0.855382514,0.932160368,158,3.511111111,31.6,19.67857143,0.308132095,0.034236899,2.773188856,6.118097198,32.77768137,3.277768137,1.515578229,32.77768137,3.277768137,52.23678852,65.55536274,6.555536274,13.2666611,32.77768137,7.311218384,10.22734484,14.07689049,17.37196889,20.98136027,2.878126441,0.287812644,0.459109869,0.062731007,0.006273101,-1.202517742,8.650312188,0.865031219,0.937031781,158,-14.45936702,3.888024175,0,18,1.8,1.255272505,4.235660391,0.423566039,4.132780372,13.0706908,1.30706908,8.762333041,5.109111885,4.007333185,5.231108617,6.52649486,7.85902698,9.214232787,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.954011122,17.32780492,8.986598879,0.998510987,80.87938991,1.542591551,3.078049832,0.307804983,0.488275647,1,0.1,2,6.156099665,0.615609966,2.046347853,2.621963198,1.819158443,0,1.644912301,0,1.542922129,3.115355072,0.311535507,0.493507552,0.298858491,0.029885849,-0.524534401,8.554710571,0.855471057,0.932205321,12.80636338,0.284585853,2.561272676,3.373575501,0.374841722,30.36217951,2.505180768,5.867786968,0.586778697,0.768474338,2.707556429,0.270755643,4.341933877,11.73557394,1.173557394,4.715434921,3.371717481,3.014081044,2.887100403,4.420977319,4.990567602,6.158555507,2.876309257,0.287630926,0.458835579,0.183857134,0.018385713,-0.735519514,8.556822595,0.855682259,0.932312528,158,3.511111111,31.6,19.67857143,0.308132095,0.034236899,2.773188856,6.118097198,32.77768137,3.277768137,1.515578229,32.77768137,3.277768137,52.23678852,65.55536274,6.555536274,13.2666611,32.77768137,7.311218384,10.22734484,14.07689049,17.37196889,20.98136027,2.878126441,0.287812644,0.459109869,0.062731007,0.006273101,-1.202517742,8.650312188,0.865031219,0.937031781,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,152.625,2.775,30.5,22.25634924,0.317189256,0.035243251,2.854703304,6.062276076,62.93280857,31.59140428,3.159140428,1.499568931,31.59140428,3.159140428,50.87529316,63.13280857,6.313280857,12.66599324,31.59140428,0.223143551,7.250702046,10.09341665,13.9423487,17.17570037,20.76777347,2.876715673,0.287671567,0.458896939,0.105803197,0.01058032,-0.97550121,8.648093523,0.864809352,0.93692037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+UMLYSI0223,L,3-methylbutyl acetate,CC(C)CCOC(=O)C,saturated 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+UMLYSI0246,L,2-methylpropyl propanoate,CCC(=O)OCC(C)C,saturated 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+UMLYSI0255,L,ethyl 3-methylbut-2-enoate,CCOC(=O)C=C(C)C,unsaturated 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+UMLYSI0260,L,methyl (E)-pent-3-enoate,C/C=C/CC(=O)OC,unsaturated 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+UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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+UMLYSI0283,L,butyl acetate,CCCCOC(=O)C,saturated 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alkanes,C7H16,142-82-5,8900,PubChem,,36,100.23,4.357826087,11.2144,22.0688,13.0912,26.3216,0.487582609,0.959513043,0.569182609,1.144417391,0.285714286,23,7,2,22,6,0,6,4,0.181818182,0,0,0,16,7,0,0,0,0,0,0,0,0,0,0,0,0,69.56521739,30.43478261,0,0,0,7,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,22,22,22,30.74,23.5256,20,20,20,20,28.08,21.4128,4.5,4.5,2,2,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,14,0,1.707106781,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,202,146,146,7.297142506,54,56,7,6,6,6,0.452380952,0.219387755,0.096598639,0.042857143,0.138888889,0.245694444,0.982777778,7.634054137,11.5,1.642857143,5.496696467,3.821367205,0.166737152,-0.026921964,0.10782785,0.031746032,7.142857143,2.621867655,0.853070277,0.001031181,-0.107445869,0.247945507,0.000358258,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,20.48528137,1.840004522,19.78207923,3.414213562,3.414213562,2.888448395,3.250213275,104.041925,20.39044053,0.886540893,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,4,0.571428571,13.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,0.886540893,1.149229762,2.024260067,2.566190217,2.805745652,2.609069045,20.39044053,26.43228451,46.55798153,59.02237498,64.53214998,60.00858803,0.195982923,0.254054166,0.447492504,0.567294146,0.620251403,0.576773143,3.637021063,3.374332195,2.499301889,1.95737174,1.717816305,1.914492911,0.19880132,0.257707677,0.453927819,0.575452308,0.629171135,0.58506762,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,56,2,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,33.2507646,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,0,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,2,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,33.2507646,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,0,5.973809612,8.168769824,11.3726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alkanone,C7H14O,12/6/7379,23847,PubChem,,35.9,114.21,5.191363636,11.4024,21.5125,12.7843,25.1158,0.518290909,0.977840909,0.581104545,1.141627273,0.25,22,8,4,21,7,1,8,3,0.142857143,1,0,0,14,7,0,1,0,0,0,0,0,0,0,0,1,0,63.63636364,31.81818182,0,4.545454545,0,6,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,30,72,83.73553719,32.1037111,96.8,79.500112,30,56,59.63636364,31.99333888,82.36,60.785424,5.666666667,4.75,2.027777778,1.666666667,2,8,2.828427125,3.583518938,1.411764706,1.56508458,0.528256863,17,2,1.37492571,7,22.7132832,2.777460299,5.19712921,0.666666667,33,4.125,0.65625,3.125,12,44,12,1.561278124,5,2.5,5,2.5,254,382,175,342,7.977658423,54,70,7.675324675,3.649726375,3.379468568,3.501604363,0.541666667,0.287690781,0.09243112,0.031702899,1.284722222,3.742824074,7.541759259,16.34249339,19,2.375,5.772900051,4.064807754,0.058043817,0.022024823,-0.071642195,0.010203255,7.875,2.968550645,1.045989943,0.000328773,0.062656093,-0.143631753,0.000101807,24,1.5,2.079441542,3.433987204,4.110873864,4.844187086,5.556828062,6.289715571,7.01301579,7.744569809,8.471568013,9.202207389,2.708050201,0,3.850147602,0,5.176149733,0,6.577861358,0,8.011355109,2.079441542,2.197224577,2.079441542,1.609437912,1.098612289,0,0,0,0,0,2.197224577,2.397895273,2.397895273,1.791759469,1.098612289,0,0,0,0,0,9.859535665,3.610917913,3.784189634,1.047985505,0.173271721,2.740584594,2.250498705,6.568914101,3.680739213,3.009975955,1.882088123,0.788675135,0.333333333,0.821114263,0.525819888,0.376246994,0.268869732,0.197168784,0.166666667,5.899812122,3.20815999,2.371013381,1.140350191,0.535074775,0.288675135,0.737476515,0.45830857,0.296376673,0.16290717,0.133768694,0.144337567,6.568914101,3.680739213,3.009975955,1.882088123,0.788675135,0.333333333,22.66178554,1.788495438,27.7911812,3.194257322,3.613264398,2.618392545,2.97130605,98.10749561,25.15304225,1.143320102,2.189391747,4.636375967,2.25,2.967432933,61.30296891,324.5463177,1.5,2.842370993,7,0.875,22,2.222274414,1.299903619,18.39747893,0.849205499,1.189826589,2.609981172,1.143320102,1.820438145,2.889916845,3.265583323,3.265583323,2.860180448,25.15304225,40.0496392,63.5781706,71.8428331,71.8428331,62.92396987,0.256382472,0.408222011,0.648046005,0.732286893,0.732286893,0.641377802,3.316111517,2.638993473,1.569514773,1.193848296,1.193848296,1.59925117,0.256382472,0.408222011,0.648046005,0.732286893,0.732286893,0.641377802,25.76517449,4.323526474,0.540440809,0.508670498,2.074313293,0.259289162,4.148626586,8.647052947,1.080881618,3.33220451,2.900309802,2.591794958,0.32397437,0.330880513,0.01080009,0.001350011,-1.573258115,6.543951602,0.81799395,0.652672063,70,2.5,17.5,14.73333333,0.452974194,0.064710599,3.170819357,4.9698133,18.18147764,2.272684705,1.007703341,18.18147764,2.272684705,27.13286733,36.36295528,4.54536941,10.21880911,18.18147768,6.070737728,8.562740006,11.66038812,14.47232617,17.41662318,2.509642727,0.313705341,0.31968952,0.105383517,0.01317294,-0.78178185,6.710512208,0.838814026,0.661404537,70,-8.118872577,2.22928515,0,14,1.75,0.916902429,4.641186476,0.58014831,4.416899332,11.05828432,1.38228554,6.767343125,5.082415225,3.80666249,5.105945474,6.49375384,7.928406026,9.392411898,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.920184803,14.05985573,6.968636025,0.995519432,48.78045218,1.34917864,2.403775915,0.300471989,0.304715183,1,0.125,2,4.80755183,0.600943979,1.704748092,2.412878418,1.620487749,0,1.440727013,0,1.33525598,2.750794013,0.343849252,0.351566456,0,0,0,6.548508152,0.818563519,0.652913898,10.10777778,0.360992063,2.526944444,2.558547764,0.365506823,17.90983435,2.288132279,4.527647899,0.565955987,0.524698117,2.764294688,0.345536836,4.46836837,9.055295797,1.131911975,3.632325892,3.19574249,2.785658533,2.782160718,4.309309027,4.987884204,6.177956811,2.532022741,0.316502843,0.322774082,0.009052933,0.001131617,-1.634568565,6.547999583,0.818499948,0.652886914,70,2.5,17.5,14.73333333,0.452974194,0.064710599,3.170819357,4.9698133,18.18147764,2.272684705,1.007703341,18.18147764,2.272684705,27.13286733,36.36295528,4.54536941,10.21880911,18.18147768,6.070737728,8.562740006,11.66038812,14.47232617,17.41662318,2.509642727,0.313705341,0.31968952,0.105383517,0.01317294,-0.78178185,6.710512208,0.838814026,0.661404537,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,65.875,1.829861111,16.4375,17.41399965,0.481204286,0.068743469,3.368429999,4.883452752,34.10792431,17.17896215,2.147370269,0.987997538,17.17896215,2.147370269,26.13095327,34.29542431,4.286928038,9.5948276,17.17896227,0.223143551,5.97516048,8.367896208,11.44739496,14.1789405,17.08199232,2.507670756,0.313458844,0.319416412,0.120565753,0.015070719,-0.735020829,6.709674881,0.83870936,0.661361181,65.8765,1.829902778,16.43795,17.41076486,0.481190573,0.06874151,3.368334013,4.883488244,34.10932449,17.17936224,2.14742028,0.98800563,17.1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compounds,C7H14O3,623-96-1,69344,PubChem,,34.5,146.21,6.092083333,12.832,24.1671,13.6933,27.5346,0.534666667,1.0069625,0.570554167,1.147275,0.2,24,10,3,23,9,1,10,6,0.260869565,1,0,0,14,7,0,3,0,0,0,0,0,0,0,0,3,0,58.33333333,29.16666667,0,12.5,0,6,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,142,177.2066116,45.85189625,190.86,152.697056,35,108,122.5454545,42.64113239,169.04,117.44096,6.333333333,4.75,2.666666667,1.791666667,1,9,3,5.257495372,1.578947368,1.691726385,0.436124566,23,1,1.201312089,8,33.69463969,3.797660098,5.83121472,1,61,6.1,1.12,5.76,21,88,20,2.246439345,8,3.311111111,8,3.311111111,541,964,425,1124,10.69962648,105,149,9.597402597,6.713716797,6.597402597,6.443424302,0.493333333,0.24982906,0.11663438,0.066096256,2.218796296,3.516388889,11.56111111,16.55246251,25.66666667,2.566666667,6.901805996,4.481754126,0.018417917,0.007783947,-0.026104624,0.001252288,8.066666667,3.675880803,1.26968644,3.95692E-05,0.019799689,-0.04743374,5.33E-06,22,2.246439345,2.302585093,3.610917913,4.262679877,4.96284463,5.645446898,6.354370041,7.048386409,7.760040681,8.458504195,9.171079658,2.944438979,0,4.007333185,0,5.231108617,0,6.532334292,0,7.878155337,2.302585093,2.302585093,2.197224577,2.079441542,1.945910149,1.386294361,1.098612289,0.693147181,0,0,2.397895273,2.48490665,2.397895273,2.302585093,2.197224577,1.386294361,1.098612289,0.693147181,0,0,9.849348106,4.025351691,4.17438727,1.037024238,0.149035579,2.978202856,2.429006717,7.819990956,4.808060412,3.389628766,2.100953631,1.249509283,0.90104757,0.781999096,0.534228935,0.376625418,0.262619204,0.178501326,0.150174595,6.553171996,3.603936267,1.946923425,0.848074696,0.412457479,0.225592232,0.6553172,0.400437363,0.216324825,0.106009337,0.058922497,0.037598705,7.819990956,4.808060412,3.389628766,2.100953631,1.249509283,0.90104757,31.24220933,2.182440934,37.76575545,3.522905582,4.473904056,2.944598175,3.395664559,110.0391,32.32975915,1.347073298,2.75907983,5.262596461,2.521928095,3.285553869,124.1585923,784.1268728,1.295461844,3.239097918,6,0.6,25.21928095,2.71928147,1.380538476,27.23909308,0.651882599,1.007443326,1.751451784,1.347073298,2.03067213,2.938721876,2.938721876,2.938721876,2.938721876,32.32975915,48.73613111,70.52932501,70.52932501,70.52932501,70.52932501,0.293802468,0.442898307,0.640947854,0.640947854,0.640947854,0.640947854,3.237889203,2.554290371,1.646240625,1.646240625,1.646240625,1.646240625,0.293802468,0.442898307,0.640947854,0.640947854,0.640947854,0.640947854,43.27254371,5.959003237,0.595900324,0.775173621,2.028523488,0.202852349,4.057046976,11.91800647,1.191800647,4.394449155,3.114674477,2.847079481,0.284707948,0.454399591,0.153454161,0.015345416,-0.814021332,8.546548771,0.854654877,0.931790775,149,3.311111111,29.8,20.02738095,0.333157677,0.03701752,2.998419094,5.991464547,31.09689182,3.109689182,1.492716983,31.09689182,3.109689182,47.67861055,62.19378363,6.219378363,13.2497693,31.09689182,7.169350017,10.13463896,13.8281059,17.14101845,20.64546363,2.673467888,0.267346789,0.427074972,0,0,0,8.673332129,0.867333213,0.938185977,149,-13.63573219,3.316343748,0,18,1.8,1.255272505,4.377202854,0.437720285,4.256588096,13.0609573,1.30609573,8.752265314,5.124278453,4.007333185,5.231108617,6.532334292,7.878155337,9.253495642,3.16227766,0.316227766,0.5,3.16227766,0.316227766,0.5,8.581163303,0.85811633,0.933546167,0.961612082,17.45945742,8.976015514,0.997335057,80.78413962,1.555480766,3.34334402,0.334334402,0.524181067,1,0.1,2,6.686688041,0.668668804,2.11147503,2.628125014,1.84582669,0,1.652923024,0,1.542427202,3.115355072,0.311535507,0.493507552,0.022716116,0.002271612,-1.643665926,8.552012809,0.855201281,0.932068343,12.95616355,0.287914746,2.59123271,3.357786759,0.373087418,30.22008083,2.511944667,5.81397518,0.581397518,0.764473173,2.79540204,0.279540204,4.466562422,11.62795036,1.162795036,4.696674299,3.390199122,3.015691861,2.913559541,4.462165709,5.097299558,6.286414413,2.78979442,0.278979442,0.445572201,0.112063525,0.011206352,-0.950535722,8.549836308,0.854983631,0.9319578,149,3.311111111,29.8,20.02738095,0.333157677,0.03701752,2.998419094,5.991464547,31.09689182,3.109689182,1.492716983,31.09689182,3.109689182,47.67861055,62.19378363,6.219378363,13.2497693,31.09689182,7.169350017,10.13463896,13.8281059,17.14101845,20.64546363,2.673467888,0.267346789,0.427074972,0,0,0,8.673332129,0.867333213,0.938185977,45,1,9,45,1,0.111111111,9,3.828641396,9,0.9,0.954242509,9,0.9,10,18,1.8,8.318498321,9.000531868,4.510859507,6.580639137,8.790421307,10.9859874,13.18336628,0,0,0,0,0,0,0,0,0,121.625,2.211363636,24.25,26.93216964,0.414263531,0.046029281,3.728371777,5.641380558,50.54995101,25.64997551,2.564997551,1.409086955,25.64997551,2.564997551,39.07985279,51.14995101,5.114995101,11.58901864,25.64997551,0.559615788,6.781022154,9.563528423,13.05333163,16.18187574,19.48779924,2.631759437,0.263175944,0.420246189,0,0,0,8.682792891,0.868279289,0.938659442,121.6799,2.212361818,24.26116,26.91612303,0.414056405,0.046006267,3.726507641,5.642179109,50.57383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+UMLYSI0303,L,3-methoxy-3-methylbutan-1-ol,CC(C)(CCO)OC,other multi-oxygen compounds,C6H14O2,56539-66-3,62118,PubChem,,33.2,118.2,5.372727273,11.1172,21.8398,12.2388,25.3252,0.505327273,0.992718182,0.556309091,1.151145455,0.25,22,8,4,21,7,0,7,3,0.142857143,0,0,0,14,6,0,2,0,0,0,0,0,0,0,0,2,0,63.63636364,27.27272727,0,9.090909091,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,32,88,107.8852158,36.98224072,114.82,94.74760144,32,60,66.06060606,35.65445462,90.96,68.645104,5.75,4.616666667,2,1.183333333,3,9,3,3.465735903,1.391304348,1.542210825,0.537158271,18,2,1.19235154,7,22.35055152,2.638993315,5.086326547,0.666666667,32,4,0.5,2.8125,11,46,12,1.5,5,2.392857143,5,2.392857143,244,424,163,475,7.847063842,52,67,7.922077922,3.049668929,3.602663936,3.019964362,0.554166667,0.282967836,0.091282895,0.015625,1.407986111,2.452986111,7.942083333,14.27203725,19.75,2.46875,5.591511471,3.851263724,0.044977544,0.024063093,-0.076135762,0.007652985,7.96875,2.936296453,1.020094495,0.000301839,0.063323957,-0.145566546,8.99498E-05,24,1.5,2.079441542,3.496507561,4.17438727,4.990432587,5.693732139,6.517671273,7.22620901,8.0522965,8.762019954,9.588708312,2.708050201,0,3.931825633,0,5.361292166,0,6.86171134,0,8.385944905,2.079441542,2.302585093,2.079441542,1.609437912,0.693147181,0,0,0,0,0,2.079441542,2.302585093,2.079441542,1.609437912,0.693147181,0,0,0,0,0,10.19346698,3.610917913,3.610917913,1,0,2.7363824,2.254805845,6.621320344,3.621320344,3.267766953,1.883883476,0.853553391,0.176776695,0.827665043,0.517331478,0.363085217,0.269126211,0.213388348,0.176776695,5.769675448,2.782153592,2.437423582,1.266451329,0.371312417,0.045643546,0.721209431,0.397450513,0.270824842,0.180921618,0.092828104,0.045643546,6.621320344,3.621320344,3.267766953,1.883883476,0.853553391,0.176776695,23.49568901,1.736543755,28.744119,2.718907669,3.370802501,2.163083419,2.497846022,98.10749561,27.2947517,1.240670532,2.099058711,4.649763026,2.405639062,2.96531293,58.7736439,311.5341228,1.405639062,2.807354922,7,0.875,22,2.116729297,1.304121287,18.29373854,0.931000364,1.25913758,3.070635056,1.240670532,2.181899058,3.265583323,3.265583323,3.265583323,2.860180448,27.2947517,48.00177927,71.8428331,71.8428331,71.8428331,62.92396987,0.278212705,0.489277389,0.732286893,0.732286893,0.732286893,0.641377802,3.218761087,2.277532561,1.193848296,1.193848296,1.193848296,1.59925117,0.282463768,0.496753501,0.743476167,0.743476167,0.743476167,0.651177994,25.3492424,4.259629201,0.53245365,0.503497436,2.156639379,0.269579922,4.313278757,8.519258402,1.0649073,3.258096538,2.912496854,2.552449407,0.319056176,0.325565714,0.436096047,0.054512006,-0.28833428,6.529170654,0.816146332,0.651886416,67,2.392857143,16.75,15.03333333,0.48191165,0.068844521,3.373381553,4.882801923,17.44264589,2.180330737,0.993289891,17.44264589,2.180330737,25.36163497,34.88529179,4.360661473,10.15615107,17.44264597,5.96870756,8.464003363,11.47977191,14.27456762,17.1623407,2.53736567,0.317170709,0.323506448,0.116490414,0.014561302,-0.746967849,6.717902604,0.839737825,0.661786963,67,-7.770920895,1.970157536,0,14,1.75,0.916902429,5.173197841,0.64664973,4.919411029,10.96036971,1.370046214,6.72982407,5.40716846,3.850147602,5.241747015,6.751101469,8.319961022,9.92088533,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.905330086,14.48528137,6.958634325,0.994090618,48.71044028,1.337802448,2.3911917,0.298898963,0.302891516,1,0.125,2,4.7823834,0.597797925,1.693319396,2.410003684,1.609437912,0,1.41706602,0,1.304948722,2.737460871,0.342182609,0.349878336,0.377964473,0.047245559,-0.338039216,6.536487976,0.817060997,0.652275573,10.31777778,0.368492063,2.579444444,2.476066668,0.35372381,17.33246667,2.301780896,4.475992721,0.55949909,0.520711497,2.859310651,0.357413831,4.610785777,8.951985441,1.11899818,3.56490681,3.24110399,2.7931441,2.880323208,4.408983917,5.158116151,6.368310339,2.473163292,0.309145412,0.314602233,0.305027059,0.038128382,-0.412529306,6.534919462,0.816864933,0.652192192,67,2.392857143,16.75,15.03333333,0.48191165,0.068844521,3.373381553,4.882801923,17.44264589,2.180330737,0.993289891,17.44264589,2.180330737,25.36163497,34.88529179,4.360661473,10.15615107,17.44264597,5.96870756,8.464003363,11.47977191,14.27456762,17.1623407,2.53736567,0.317170709,0.323506448,0.116490414,0.014561302,-0.746967849,6.717902604,0.839737825,0.661786963,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,61,1.694444444,15.1875,17.38637637,0.528997248,0.075571035,3.702980739,4.70896698,31.00908221,15.7545411,1.969317638,0.957924606,15.7545411,1.969317638,23.05410272,31.38408221,3.923010276,9.368913402,15.75454158,0.405465108,5.764014863,8.155676186,11.07520036,13.76375569,16.55267028,2.601446676,0.325180835,0.332171943,0.155170227,0.019396278,-0.647353284,6.702375829,0.837796979,0.660983021,61.0144,1.694844444,15.19125,17.37954747,0.528872116,0.075553159,3.702104811,4.709391528,31.01827876,15.758539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alkanone,C6H12O,108-10-1,7909,PubChem,,32.8,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,89.16,75.902548,24,48,51.63636364,25.32889259,69.47,53.96848,5.166666667,4.25,1.666666667,1.25,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.310313182,4,18.18873053,2.526054707,4.585845777,1,24,3.428571429,0.653061224,2.612244898,10,26,6,1.378783493,4,2.285714286,4,2.285714286,174,289,114,265,6.786218125,38,48,6.675324675,2.784001567,5.675324675,2.654873479,0.547619048,0.19047619,0.095238095,0,1.37962963,3.254166667,6.680555556,11.32250989,17.5,2.5,4.956403471,3.404799764,0.070637856,-0.040895839,0.125955654,0.017766811,8,2.578755647,0.887089618,0.000849145,-0.110482124,0.247416362,0.000364376,21,1.378783493,1.945910149,3.295836866,3.891820298,4.65396035,5.262690189,6.033086222,6.64509097,7.417580402,8.030409562,8.803424212,2.564949357,0,3.713572067,0,4.976733742,0,6.300785795,0,7.655864018,1.945910149,2.079441542,1.609437912,1.609437912,0,0,0,0,0,0,2.079441542,2.302585093,1.791759469,1.945910149,0,0,0,0,0,0,9.46962297,3.36729583,3.555348061,1.055846662,0.188052232,2.603368154,2.145270715,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,0.837401046,0.520982943,0.431913709,0.23570226,0.23570226,0,5.192705341,2.620626365,2.299986098,0.695705769,0.574914957,0,0.741815049,0.436771061,0.328569443,0.173926442,0.143728739,0,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,19.33273622,1.654068949,21.90865123,2.606723697,3.045939258,2.116654072,2.370974343,80.71062276,22.18129774,1.167436723,1.956066972,4.230388542,1.378783493,2.779475245,41.07740642,203.05865,1.378783493,2.625814584,6,0.857142857,17.65148445,2.001032983,1.23863892,14.72926917,0.875221883,1.20739461,2.67238404,1.167436723,1.878477513,2.970840013,2.970840013,2.747264881,1.95822751,22.18129774,35.69107275,56.44596025,56.44596025,52.19803274,37.20632268,0.27482501,0.44221035,0.699362219,0.699362219,0.64673064,0.46098421,3.08049079,2.36945,1.2770875,1.2770875,1.500662633,2.289700004,0.27482501,0.44221035,0.699362219,0.699362219,0.64673064,0.46098421,18.75538595,3.414213562,0.487744795,0.373303478,2,0.285714286,4,6.828427125,0.975489589,2.772588722,2.765108219,2.5,0.357142857,0.278558006,0.5,0.071428571,-0.210720997,5.560090548,0.79429865,0.521557305,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,48,-6.426891805,2.132600461,0,12,1.714285714,0.755426872,4.414213562,0.630601937,4.14626437,9.435385883,1.347912269,5.82008293,4.927315195,3.663561646,4.976733742,6.368187186,7.788626066,9.222170124,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.893113616,11.82718272,5.981268914,0.996878152,35.88761348,1.222681352,1.816496581,0.259499512,0.181464209,1,0.142857143,2,3.632993162,0.518999023,1.466337069,2.28143274,1.466337069,0,1.358123484,0,1.278874112,2.562948829,0.366135547,0.286117955,0.562948829,0.080421261,-0.174671756,5.561848899,0.794549843,0.52165343,8.444444444,0.402116402,2.412698413,2.248851359,0.37480856,13.49310816,2.136940824,3.815288462,0.545041209,0.407069165,2.606803347,0.372400478,4.261858866,7.630576925,1.090082418,3.282657138,3.027112858,2.639415511,2.603311063,4.094262329,4.675754999,5.840902485,2.447753146,0.349679021,0.272137332,0.351391722,0.050198817,-0.317945932,5.563998554,0.794856936,0.521770906,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,44.5,1.589285714,12.67857143,14.38485735,0.528133809,0.088022301,3.168802852,4.400027701,25.96314285,13.10657143,1.872367347,0.782242369,13.10657143,1.872367347,20.3329338,26.14171428,3.734530612,8.087861145,13.10657758,0.223143551,5.454252168,7.528209951,10.38174844,12.81313161,15.46640683,2.4526457,0.350377957,0.272744371,0.068013005,0.009716144,-0.817185628,5.725615498,0.817945071,0.530475526,44.5012,1.589328571,12.679,14.38170258,0.528116397,0.088019399,3.16869838,4.400078348,25.96457826,13.10698913,1.872427018,0.782252057,13.10698913,1.872427018,20.3335127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alkanone,C6H12O,565-69-5,11265,PubChem,,30.7,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,91,75.254912,26,52,55.63636364,27.99333888,76.11,56.446384,5.166666667,4.25,1.777777778,1.416666667,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.354079995,6,17.8730874,2.390457219,4.503855938,1,23,3.285714286,0.612244898,2.408163265,9,29,8,1.448815636,4,2.19047619,4,2.19047619,168,273,106,233,6.66900067,36,46,6.675324675,2.784001567,2.380130167,2.654873479,0.557142857,0.304761905,0.05952381,0,1.319444444,3.511574074,7.023148148,12.18938907,17.5,2.5,4.956403471,3.398141088,0.070637856,-0.040895839,0.126121826,0.017672557,8,2.585571719,0.897277662,0.000860648,-0.110271788,0.246577436,0.000374291,21,1.378783493,1.945910149,3.295836866,3.970291914,4.691347882,5.398162702,6.120297419,6.834108739,7.556427969,8.272059622,8.994296558,2.564949357,0,3.713572067,0,5.056245805,0,6.463029457,0,7.890956716,1.945910149,2.079441542,1.945910149,1.098612289,0,0,0,0,0,0,2.079441542,2.302585093,2.302585093,1.098612289,0,0,0,0,0,0,9.660268837,3.36729583,3.555348061,1.055846662,0.188052232,2.60307797,2.150292971,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,0.837401046,0.530123202,0.375649873,0.297003623,0.23570226,0,5.192705341,2.70815999,1.974566772,0.992412263,0.40824829,0,0.741815049,0.451359998,0.282080967,0.165402044,0.204124145,0,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,19.66178554,1.627173667,22.38932109,2.694257322,3.113264398,2.190321638,2.483659956,80.71062276,22.18129774,1.167436723,1.884172925,4.251185325,2.521640636,2.776119445,39.56763142,195.554525,1.378783493,2.625814584,6,0.857142857,17.65148445,1.970221248,1.245613437,14.55360052,0.96551028,1.270647212,3.253112643,1.167436723,1.733483434,2.535857776,2.970840013,2.747264881,2.076539484,22.18129774,32.93618524,48.18129774,56.44596025,52.19803274,39.4542502,0.27482501,0.408077452,0.596963523,0.699362219,0.64673064,0.488835903,3.08049079,2.51444408,1.712069738,1.2770875,1.500662633,2.171388029,0.27482501,0.408077452,0.596963523,0.699362219,0.64673064,0.488835903,19.08443528,3.831493958,0.54735628,0.408357701,2.05288084,0.293268691,4.10576168,7.662987915,1.094712559,2.890371758,2.766317013,2.45983181,0.351404544,0.273633789,0.093061608,0.013294515,-0.721860612,5.552761685,0.793251669,0.521156324,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,46,-6.159104646,1.776169319,0,12,1.714285714,0.755426872,4.628651282,0.661235897,4.306931632,9.710395916,1.387199417,5.80814249,4.971323474,3.663561646,4.976733742,6.381816017,7.833203949,9.311722986,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.908782632,12.05985573,5.968049074,0.994674846,35.80829445,1.246819195,2.201850425,0.314550061,0.239951469,1,0.142857143,2,4.40370085,0.629100121,1.563975538,2.292173574,1.516347489,0,1.325862622,0,1.230370276,2.562948829,0.366135547,0.286117955,0.169101979,0.024157426,-0.540295917,5.555979679,0.793711383,0.521332454,8.541666667,0.406746032,2.44047619,2.23350476,0.372250793,13.40102856,2.143651097,3.87952078,0.554217254,0.412144658,2.672307736,0.381758248,4.365973724,7.759041561,1.108434509,3.143621215,3.04993562,2.641823012,2.639682341,4.150095046,4.80007426,5.972970241,2.406659833,0.343808548,0.266990297,0.061390182,0.008770026,-0.848330755,5.556666987,0.79380957,0.521370059,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.10714286,14.63437002,0.567501699,0.094583616,3.405010193,4.313521925,25.02490021,12.6374501,1.805350015,0.771161618,12.6374501,1.805350015,18.84162152,25.20347164,3.600495948,8.063131807,12.63745986,0.223143551,5.350672597,7.470308712,10.2053365,12.653946,15.23397362,2.323830709,0.331975816,0.256343141,0.03786306,0.005409009,-0.995249002,5.750979372,0.821568482,0.531819267,42.5012,1.5179,12.10757143,14.63116157,0.567480492,0.094580082,3.40488295,4.313569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+UMLYSI0325,L,"2,2-dimethylbutanoic acid",CCC(C)(C)C(=O)O,saturated 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alkanone,C6H12O,565-61-7,11262,PubChem,,29,100.18,5.272631579,9.8756,18.6289,11.0229,21.7006,0.519768421,0.980468421,0.580152632,1.142136842,0.285714286,19,7,4,18,6,1,7,2,0.111111111,1,0,0,12,6,0,1,0,0,0,0,0,0,0,0,1,0,63.15789474,31.57894737,0,5.263157895,0,5,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,68,79.73553719,28.1037111,90.5,75.363288,26,51,54.63636364,27.66111574,74.07,55.879184,5.166666667,4.25,1.777777778,1.375,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,15,2,1.34275231,6,17.8730874,2.390457219,4.503855938,1,23,3.285714286,0.612244898,2.408163265,9,29,8,1.448815636,4,2.19047619,4,2.19047619,168,278,106,245,6.66900067,36,46,6.675324675,2.784001567,2.380130167,2.654873479,0.557142857,0.304761905,0.05952381,0,1.372685185,3.374768519,6.789351852,11.83892591,17.5,2.5,4.956403471,3.408142459,0.070637856,-0.040895839,0.125873226,0.017813751,8,2.58170456,0.89267475,0.00085411,-0.11039099,0.246965044,0.000369677,21,1.378783493,1.945910149,3.295836866,3.970291914,4.691347882,5.398162702,6.120297419,6.834108739,7.556427969,8.272059622,8.994296558,2.564949357,0,3.713572067,0,5.056245805,0,6.463029457,0,7.890956716,1.945910149,2.079441542,1.945910149,1.098612289,0,0,0,0,0,0,2.079441542,2.302585093,2.197224577,1.386294361,0,0,0,0,0,0,9.660268837,3.36729583,3.555348061,1.055846662,0.188052232,2.60307797,2.150292971,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,0.837401046,0.530123202,0.375649873,0.297003623,0.23570226,0,5.192705341,2.685504621,1.919946271,1.306356179,0.287457479,0,0.741815049,0.447584103,0.274278039,0.21772603,0.143728739,0,5.86180732,3.180739213,2.629549111,1.782021738,0.471404521,0,19.66178554,1.627173667,22.38932109,2.671601953,3.104613364,2.168030877,2.4575601,80.71062276,22.18129774,1.167436723,1.884172925,4.251185325,2.521640636,2.776119445,39.56763142,195.554525,1.378783493,2.625814584,7,1,19.65148445,1.970221248,1.245613437,14.55360052,0.96551028,1.270647212,3.253112643,1.167436723,1.878477513,3.076103171,3.391892645,3.168317512,2.274016983,22.18129774,35.69107275,58.44596025,64.44596025,60.19803274,43.20632268,0.27482501,0.44221035,0.724142105,0.798481762,0.745850183,0.535323867,3.08049079,2.36945,1.171824342,0.856034869,1.079610001,1.97391053,0.27482501,0.44221035,0.724142105,0.798481762,0.745850183,0.535323867,19.08443528,3.831493958,0.54735628,0.408357701,2.05288084,0.293268691,4.10576168,7.662987915,1.094712559,2.890371758,2.766317013,2.45983181,0.351404544,0.273633789,0.093061608,0.013294515,-0.721860612,5.552761685,0.793251669,0.521156324,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,46,-6.159104646,1.776169319,0,12,1.714285714,0.755426872,4.628651282,0.661235897,4.306931632,9.710395916,1.387199417,5.80814249,4.971323474,3.663561646,4.976733742,6.381816017,7.833203949,9.311722986,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.908782632,12.05985573,5.968049074,0.994674846,35.80829445,1.246819195,2.201850425,0.314550061,0.239951469,1,0.142857143,2,4.40370085,0.629100121,1.563975538,2.292173574,1.516347489,0,1.325862622,0,1.230370276,2.562948829,0.366135547,0.286117955,0.169101979,0.024157426,-0.540295917,5.555979679,0.793711383,0.521332454,8.541666667,0.406746032,2.44047619,2.23350476,0.372250793,13.40102856,2.143651097,3.87952078,0.554217254,0.412144658,2.672307736,0.381758248,4.365973724,7.759041561,1.108434509,3.143621215,3.04993562,2.641823012,2.639682341,4.150095046,4.80007426,5.972970241,2.406659833,0.343808548,0.266990297,0.061390182,0.008770026,-0.848330755,5.556666987,0.79380957,0.521370059,46,2.19047619,13.14285714,12,0.524034701,0.087339117,3.144208207,4.430816799,13.63462203,1.947803147,0.794250172,13.63462203,1.947803147,19.85726871,27.26924405,3.895606293,8.706490362,13.63462517,5.484796933,7.721791777,10.49573647,13.04245616,15.68479403,2.350941918,0.335848845,0.25986933,0.048608437,0.006944062,-0.919301837,5.750849644,0.821549949,0.531812409,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.10714286,14.59294701,0.565515571,0.094252595,3.393093426,4.30512028,24.92301184,12.58650592,1.798072274,0.769933629,12.58650592,1.798072274,18.60695904,25.10158327,3.585940466,8.060283462,12.58651618,0.223143551,5.338739281,7.465006667,10.18470947,12.63700883,15.20782599,2.290802482,0.327257497,0.251991351,0.127249147,0.01817845,-0.626741584,5.756701613,0.822385945,0.532121603,42.5012,1.5179,12.10757143,14.58976541,0.565495327,0.094249221,3.392971961,4.305171919,24.92442949,12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+UMLYSI0340,L,methyl (E)-but-2-enoate,C/C=C/C(=O)OC,unsaturated 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compounds,C6H14O2,105-57-7,7765,PubChem,,22.4,118.2,5.372727273,11.1172,21.8398,12.2388,25.3252,0.505327273,0.992718182,0.556309091,1.151145455,0.25,22,8,3,21,7,0,7,4,0.19047619,0,0,0,14,6,0,2,0,0,0,0,0,0,0,0,2,0,63.63636364,27.27272727,0,9.090909091,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,92,115.4710744,35.30669627,122.14,95.721696,27,67,76.09090909,32.31557036,103.07,70.65432,5.333333333,4.666666667,2.166666667,1.611111111,1,7,2.645751311,3.871201011,1.5,1.622389604,0.508249709,18,1,1.302993695,6,23.25211835,3.017804311,4.720931985,1,37,4.625,0.875,3.3125,13,46,12,1.905639062,6,2.678571429,6,2.678571429,271,431,195,411,8.219930079,61,75,7.922077922,5.066543213,4.922077922,5.017193766,0.491666667,0.255242674,0.125886613,0.031986532,1.37037037,1.56,6.24,8.029544714,17.33333333,2.166666667,5.689383877,3.565764635,0.056683864,0.022214747,-0.07874601,0.006475538,7.666666667,2.945274023,0.960998894,0.000321107,0.06284044,-0.148236895,7.5819E-05,18,1.905639062,2.079441542,3.36729583,4.007333185,4.709530201,5.370638028,6.084499413,6.751101469,7.467942332,8.135932772,8.853522561,2.708050201,0,3.761200116,0,4.990432587,0,6.304448802,0,7.656810091,2.079441542,2.079441542,1.945910149,1.791759469,1.098612289,0.693147181,0,0,0,0,2.079441542,2.079441542,1.945910149,1.791759469,1.098612289,0.693147181,0,0,0,0,9.537916008,3.610917913,3.610917913,1,0,2.751012246,2.245682124,6.405777394,3.808060412,2.682521985,1.562948829,1.129936127,0.288675135,0.800722174,0.54400863,0.383217426,0.260491471,0.225987225,0.144337567,5.808060412,3.040318622,1.478313168,0.80274943,0.517528619,0.096225045,0.726007552,0.434331232,0.211187595,0.133791572,0.103505724,0.048112522,6.405777394,3.808060412,2.682521985,1.562948829,1.129936127,0.288675135,24.66485906,1.878752807,26.51036701,2.968889104,3.496823185,2.389778356,2.807745766,98.10749561,27.2947517,1.240670532,2.363698337,4.612040278,1.905639062,2.964571277,66.18355343,345.9030208,1.405639062,2.896291529,5,0.625,18,2.370864648,1.294033502,18.5203906,0.755738641,1.102614844,2.164971808,1.240670532,1.638619964,2.526280482,2.901946959,2.901946959,2.699245522,27.2947517,36.0496392,55.5781706,63.8428331,63.8428331,59.38340148,0.278212705,0.367450407,0.566502796,0.650743684,0.650743684,0.605289138,3.218761087,2.820811655,1.933151137,1.557484659,1.557484659,1.760186097,0.282463768,0.373065015,0.575158906,0.660686986,0.660686986,0.614537899,26.65642289,4.414213562,0.551776695,0.515882673,2,0.25,4,8.828427125,1.103553391,3.433987204,2.888532276,2.598076211,0.324759526,0.331721509,0.288675135,0.036084392,-0.431672498,6.559884359,0.819985545,0.653516947,75,2.678571429,18.75,14.31666667,0.41708752,0.059583931,2.919612641,5.111987788,19.54202305,2.442752881,1.032775617,19.54202305,2.442752881,29.63985773,39.0840461,4.885505763,10.32301992,19.54202306,6.23636959,8.754160749,11.96172461,14.82466521,17.85439454,2.341896018,0.292737002,0.295654087,0,0,0,6.721964274,0.840245534,0.66199696,75,-8.698792047,2.52445867,0,14,1.75,0.916902429,4.362339833,0.545292479,4.164277569,10.37020798,1.296275997,6.828712072,4.900527807,3.761200116,4.990432587,6.304448802,7.666221926,9.058703197,2.828427125,0.353553391,0.361235995,2.828427125,0.353553391,0.361235995,6.599663291,0.824957911,0.655617423,0.952015103,13.45945742,6.967160088,0.995308584,48.77012062,1.383130245,2.511522628,0.313940329,0.319949676,1,0.125,2,5.023045256,0.627880657,1.7847908,2.426178829,1.673976434,0,1.497885427,0,1.383396648,2.776551668,0.347068958,0.354804608,0.338873676,0.042359209,-0.375969733,6.565312235,0.820664029,0.653804308,9.836944444,0.351319444,2.459236111,2.716495952,0.38807085,19.01547166,2.270682647,4.657984927,0.582248116,0.534558463,2.659874711,0.332484339,4.316355344,9.315969854,1.164496232,3.756346352,3.146791307,2.776963754,2.659716726,4.190658071,4.790273468,5.96004991,2.544377903,0.318047238,0.324465292,0.200902294,0.025112787,-0.557612084,6.563389909,0.820423739,0.653702564,75,2.678571429,18.75,14.31666667,0.41708752,0.059583931,2.919612641,5.111987788,19.54202305,2.442752881,1.032775617,19.54202305,2.442752881,29.63985773,39.0840461,4.885505763,10.32301992,19.54202306,6.23636959,8.754160749,11.96172461,14.82466521,17.85439454,2.341896018,0.292737002,0.295654087,0,0,0,6.721964274,0.840245534,0.66199696,28,1,7,28,1,0.142857143,7,3.36729583,7,0.875,0.676078432,7,0.875,8,14,1.75,6.647688374,7.003254818,4.043051268,5.82008293,7.786967003,9.729193687,11.67552889,0,0,0,0,0,0,0,0,0,62,1.722222222,15.4375,17.82808303,0.51254986,0.073221409,3.58784902,4.766970107,31.86903667,16.18451833,2.023064792,0.967279823,16.18451833,2.023064792,24.01478799,32.24403667,4.030504583,9.238599787,16.18451866,0.405465108,5.838094812,8.216451208,11.19237762,13.89273633,16.71722061,2.315080935,0.289385117,0.291652943,0,0,0,6.731433328,0.841429166,0.662486039,62.0312,1.723088889,15.44545,17.81756075,0.51226783,0.073181119,3.58587481,4.767865623,31.88628665,16.19254333,2.024067916,0.967452054,16.19254333,2.024067916,2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+UMLYSI0367,L,methyl 2-methylpropanoate,CC(C)C(=O)OC,saturated 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compounds,C5H12O2,53778-73-7,40895,PubChem,,19.8,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,3,18,6,0,6,3,0.166666667,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,80,99.88521579,28.31779443,101.16,86.3241,23,47,52,27.09704999,72.57,53.9118,4.833333333,3.7,1.916666667,1.15,1,6,2.449489743,3.17805383,1.448275862,1.574610106,0.56094383,16,1,1.239362838,5,18.44734799,2.654735212,4.126706124,0.666666667,28,4,0.571428571,2.612244898,10,30,9,1.556656707,5,2.380952381,5,2.380952381,181,321,122,354,6.946161136,45,50,6.922077922,4.090937571,3.760188211,4.045398377,0.5,0.267176871,0.095044836,0.021915584,1.606481481,2.732916667,7.754814815,10.06583005,17.83333333,2.547619048,4.856002902,3.159517476,0.061089529,-0.043229605,0.13503849,0.013284685,8.047619048,2.568402878,0.863068587,0.000821327,-0.1110028,0.251045289,0.000324427,17,1.556656707,1.945910149,3.218875825,3.850147602,4.532599493,5.187385806,5.877735782,6.538139824,7.230563153,7.892452044,8.58541243,2.564949357,0,3.610917913,0,4.844187086,0,6.150602768,0,7.486052618,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,9.28804188,3.36729583,3.36729583,1,0,2.614865278,2.142011297,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,0.814095802,0.551343402,0.383682523,0.295679568,0.232307808,0.204124145,4.847025718,2.478725677,1.517413598,0.830302717,0.31023786,0.11785113,0.692432245,0.413120946,0.252902266,0.166060543,0.10341262,0.11785113,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,21.63281953,1.723209833,21.19445472,2.413673362,3.076644828,1.982343858,2.282526329,80.71062276,24.08143227,1.267443804,2.135932757,4.21617463,2.807354922,2.773312733,44.85458789,210.8087315,1.448815636,2.688721876,7,1,19.65148445,2.205114616,1.236303072,14.74169749,0.825971181,1.158812582,2.459659311,1.267443804,2.245098682,3.536886724,3.536886724,3.536886724,3.089736459,24.08143227,42.65687495,67.20084775,67.20084775,67.20084775,58.70499272,0.298367569,0.528516241,0.83261466,0.83261466,0.83261466,0.727351502,2.98048371,2.002828832,0.71104079,0.71104079,0.71104079,1.158191054,0.303948825,0.538402653,0.848189529,0.848189529,0.848189529,0.740957321,19.84836247,3.939231012,0.562747287,0.416788015,1.969615506,0.281373644,3.939231012,7.878462024,1.125494575,2.995732274,2.752839537,2.458386904,0.351198129,0.273455164,0.215080985,0.030725855,-0.467178589,5.570942974,0.795848996,0.522150099,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,50,-6.694678963,2.195077851,0,12,1.714285714,0.755426872,4.334200532,0.619171505,4.073877841,9.130266124,1.304323732,5.869296913,4.765742035,3.610917913,4.844187086,6.167516491,7.540621529,8.944158831,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.945160118,11.45945742,5.969002809,0.994833801,35.81401685,1.284362278,2.316496581,0.330928083,0.255382164,1,0.142857143,2,4.632993162,0.661856166,1.642227735,2.30716955,1.575536361,0,1.389760569,0,1.287534953,2.590770275,0.370110039,0.289400234,0.196923425,0.028131918,-0.493991833,5.574879675,0.796411382,0.522364849,8.283055556,0.394431217,2.366587302,2.393986775,0.398997796,14.36392065,2.124241775,4.017017002,0.573859572,0.422732569,2.55690369,0.365271956,4.197913496,8.034034004,1.147719143,3.247582134,2.99553753,2.631895624,2.499241349,4.013658965,4.571570731,5.722750231,2.411173456,0.344453351,0.267559918,0.153043803,0.0218634,-0.570628976,5.574272268,0.79632461,0.522331724,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,45,1.607142857,12.78571429,13.82385392,0.524251734,0.087375289,3.145510406,4.412798293,26.04604301,13.27302151,1.896145929,0.786078859,13.27302151,1.896145929,20.23152726,26.40318587,3.771883696,8.025873991,13.2730269,0.405465108,5.467532576,7.5879122,10.41812115,12.88769586,15.53506769,2.371386667,0.338769524,0.262501662,0.02878882,0.004112689,-1.078543298,5.726309447,0.818044207,0.530512369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compounds,C5H12O2,7778-85-0,24509,PubChem,,18.6,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,3,18,6,0,6,3,0.166666667,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,24,88,111.4710744,29.09261294,111.08,93.235844,23,51,57.36363636,27.76149628,79.93,61.04772,4.833333333,4.166666667,1.916666667,0.972222222,1,6,2.449489743,3.17805383,1.448275862,1.574610106,0.56094383,16,1,1.163236268,5,18.44734799,2.654735212,4.126706124,0.666666667,28,4,0.571428571,2.612244898,10,30,9,1.556656707,5,2.380952381,5,2.380952381,181,317,122,338,6.946161136,45,50,6.922077922,4.090937571,3.760188211,4.045398377,0.5,0.267176871,0.095044836,0.021915584,1.106481481,1.365555556,5.270092593,6.456478629,15.83333333,2.261904762,4.872887296,2.974753993,0.068992871,-0.041275029,0.138125656,0.012034851,7.761904762,2.556067025,0.809047625,0.000832115,-0.110798806,0.255581738,0.00028059,17,1.556656707,1.945910149,3.218875825,3.850147602,4.532599493,5.187385806,5.877735782,6.538139824,7.230563153,7.892452044,8.58541243,2.564949357,0,3.610917913,0,4.844187086,0,6.150602768,0,7.486052618,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,1.945910149,1.945910149,1.791759469,1.386294361,0.693147181,0,0,0,0,0,9.28804188,3.36729583,3.36729583,1,0,2.614865278,2.142011297,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,0.814095802,0.551343402,0.383682523,0.295679568,0.232307808,0.204124145,5.100953631,2.326472536,1.501661279,0.803743642,0.30274943,0.068041382,0.728707662,0.387745423,0.25027688,0.160748728,0.100916477,0.068041382,5.698670613,3.308060412,2.302095141,1.478397839,0.696923425,0.204124145,22.17082433,1.723209833,21.19445472,2.235147106,2.982165584,1.80305083,2.050455312,80.71062276,24.08143227,1.267443804,2.135932757,4.21617463,2.807354922,2.773312733,44.85458789,210.8087315,1.448815636,2.688721876,7,1,19.65148445,2.205114616,1.236303072,14.74169749,0.825971181,1.158812582,2.459659311,1.267443804,1.773214355,2.970840013,3.076103171,3.391892645,2.94474238,24.08143227,33.69107275,56.44596025,58.44596025,64.44596025,55.95010522,0.298367569,0.417430464,0.699362219,0.724142105,0.798481762,0.693218604,2.98048371,2.474713158,1.2770875,1.171824342,0.856034869,1.303185133,0.303948825,0.425238908,0.712444471,0.737687889,0.813418141,0.706185934,19.84836247,3.939231012,0.562747287,0.416788015,1.969615506,0.281373644,3.939231012,7.878462024,1.125494575,2.995732274,2.752839537,2.458386904,0.351198129,0.273455164,0.215080985,0.030725855,-0.467178589,5.570942974,0.795848996,0.522150099,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,50,-6.694678963,2.195077851,0,12,1.714285714,0.755426872,4.334200532,0.619171505,4.073877841,9.130266124,1.304323732,5.869296913,4.765742035,3.610917913,4.844187086,6.167516491,7.540621529,8.944158831,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.945160118,11.45945742,5.969002809,0.994833801,35.81401685,1.284362278,2.316496581,0.330928083,0.255382164,1,0.142857143,2,4.632993162,0.661856166,1.642227735,2.30716955,1.575536361,0,1.389760569,0,1.287534953,2.590770275,0.370110039,0.289400234,0.196923425,0.028131918,-0.493991833,5.574879675,0.796411382,0.522364849,8.283055556,0.394431217,2.366587302,2.393986775,0.398997796,14.36392065,2.124241775,4.017017002,0.573859572,0.422732569,2.55690369,0.365271956,4.197913496,8.034034004,1.147719143,3.247582134,2.99553753,2.631895624,2.499241349,4.013658965,4.571570731,5.722750231,2.411173456,0.344453351,0.267559918,0.153043803,0.0218634,-0.570628976,5.574272268,0.79632461,0.522331724,50,2.380952381,14.28571429,11.61666667,0.471969994,0.078661666,2.831819966,4.605170186,14.86357869,2.123368384,0.820486371,14.86357869,2.123368384,22.55648517,29.72715738,4.246736768,8.823206221,14.86357965,5.693732139,7.932003152,10.86759655,13.45609042,16.21265509,2.394401389,0.342057341,0.265437869,0.059016506,0.008430929,-0.860318553,5.72615888,0.818022697,0.530504376,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,42.5,1.517857143,12.07142857,14.49568765,0.554215417,0.092369236,3.325292504,4.307437778,24.48025655,12.49012828,1.784304039,0.767596829,12.49012828,1.784304039,18.93917724,24.83739941,3.548199915,7.755593172,12.49014051,0.405465108,5.341735987,7.413178838,10.1707057,12.58678975,15.16850621,2.462258389,0.351751198,0.273933538,0.052554326,0.007507761,-0.89557407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compounds,C5H12O2,462-95-3,10024,PubChem,,18.5,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,2,18,6,0,6,4,0.222222222,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,86,109.4710744,27.97780368,109.62,89.9,20,52,59.27272727,24.65112406,82,58.3376,4.5,3.833333333,2,1.333333333,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,16,0,1.28445705,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,338,146,338,7.297142506,54,56,6.922077922,5.922077922,5.922077922,5.856154977,0.452380952,0.219387755,0.096598639,0.042857143,1.0625,1.351666667,5.406666667,4.623760096,15.5,2.214285714,4.932748273,2.90044366,0.074886278,-0.039955033,0.139740753,0.011428571,7.714285714,2.565966074,0.797584224,0.000853715,-0.110398222,0.256720966,0.000270545,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,4.937816924,2.568914101,1.10344969,0.574914957,0.225592232,0.11785113,0.705402418,0.42815235,0.220689938,0.143728739,0.075197411,0.058925565,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,22.48528137,1.840004522,19.78207923,2.490268736,3.102045534,2.034178074,2.36877908,80.71062276,24.08143227,1.267443804,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,4,0.571428571,13.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,1.267443804,1.667951197,2.589518303,2.589518303,2.589518303,2.36594317,24.08143227,31.69107275,49.20084775,49.20084775,49.20084775,44.95292024,0.298367569,0.392650579,0.60959569,0.60959569,0.60959569,0.556964111,2.98048371,2.579976316,1.658409211,1.658409211,1.658409211,1.881984343,0.303948825,0.399995491,0.620998771,0.620998771,0.620998771,0.567382667,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,45.5,1.625,12.92857143,14.31379731,0.510049463,0.085008244,3.060296776,4.468777561,26.55549206,13.52774603,1.932535147,0.791857809,13.52774603,1.932535147,21.35801569,26.91263492,3.844662131,7.855581014,13.5277505,0.405465108,5.540773229,7.587532225,10.52702358,12.95613961,15.66544984,2.315080935,0.330725848,0.255196325,0,0,0,5.731433328,0.81877619,0.530784271,45.5252,1.6259,12.93594286,14.30424641,0.509742772,0.084957129,3.058456633,4.469760723,26.5715211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compounds,C5H12O2,77-76-9,6495,PubChem,,16.4,104.17,5.482631579,9.5904,18.9562,10.4774,21.91,0.504757895,0.997694737,0.551442105,1.153157895,0.285714286,19,7,4,18,6,0,6,2,0.111111111,0,0,0,12,5,0,2,0,0,0,0,0,0,0,0,2,0,63.15789474,26.31578947,0,10.52631579,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,28,92,115.4710744,35.30669627,122.7,98.03201296,28,68,77.09090909,33.31557036,106.12,78.901568,5.25,4.583333333,1.75,0.916666667,3,8,2.828427125,2.772588722,1.333333333,1.485994289,0.600561204,16,2,1.112372436,6,17.55030825,2.267786838,4.393619129,1,22,3.142857143,0.489795918,2.204081633,8,32,8,1.378783493,4,2.095238095,4,2.095238095,162,290,98,258,6.544661395,34,44,6.922077922,2.286087597,2.597516519,2.260639497,0.576190476,0.302380952,0.035714286,0,1.666666667,1.364583333,5.458333333,7.018462259,16.25,2.321428571,4.791899283,2.977022898,0.061929799,-0.043010403,0.138800533,0.011777696,7.821428571,2.552084187,0.835548531,0.000817548,-0.111074878,0.253725,0.000297767,21,1.378783493,1.945910149,3.36729583,4.043051268,4.844187086,5.549076085,6.354370041,7.063903961,7.870547845,8.580918882,9.387900612,2.564949357,0,3.80666249,0,5.262690189,0,6.767343125,0,8.282230063,1.945910149,2.197224577,1.945910149,0.693147181,0,0,0,0,0,0,1.945910149,2.197224577,1.945910149,0.693147181,0,0,0,0,0,0,10.00378527,3.36729583,3.36729583,1,0,2.598436492,2.156849666,5.914213562,3.121320344,2.871320344,1.914213562,0.25,0,0.844887652,0.520220057,0.358915043,0.319035594,0.25,0,5.316496581,2.224744871,1.808078205,0.983163248,0.083333333,0,0.759499512,0.370790812,0.226009776,0.163860541,0.083333333,0,5.914213562,3.121320344,2.871320344,1.914213562,0.25,0,20.8492424,1.575449285,23.20776119,2.141328865,2.80610497,1.685146429,1.894841708,80.71062276,24.08143227,1.267443804,1.772338851,4.265583323,1.950212065,2.773989252,37.21911587,187.6856662,1.378783493,2.584962501,4,0.571428571,13.65148445,1.857776861,1.250358386,14.44425022,1.073698468,1.347900316,4.095921172,1.267443804,1.667951197,2.299530145,2.299530145,2.075955012,1.392180799,24.08143227,31.69107275,43.69107275,43.69107275,39.44314523,26.45143518,0.298367569,0.392650579,0.541329893,0.541329893,0.488698314,0.327731769,2.98048371,2.579976316,1.948397369,1.948397369,2.171972501,2.855746715,0.303948825,0.399995491,0.551455996,0.551455996,0.497839892,0.333862311,18.72792206,3.797932652,0.542561807,0.405683081,2.135779205,0.305111315,4.27155841,7.595865304,1.085123615,2.833213344,2.780225452,2.429858132,0.34712259,0.269906642,0.377377163,0.053911023,-0.296257069,5.537520834,0.791074405,0.520320761,44,2.095238095,12.57142857,12.25,0.560072524,0.093345421,3.360435143,4.343805422,13.06981118,1.867115882,0.781388519,13.06981118,1.867115882,18.30587915,26.13962235,3.734231764,8.638702609,13.0698166,5.379897354,7.621195163,10.31164902,12.83989701,15.4262662,1.946497979,0.27807114,0.20247777,0,0,0,5.80804425,0.829720607,0.534820942,44,-5.891317487,1.496019422,0,12,1.714285714,0.755426872,5.164247938,0.737749705,4.782281927,9.733118453,1.390445493,5.771441123,5.327435308,3.713572067,5.129898715,6.665683718,8.256607345,9.873801242,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.891805812,12.48528137,5.960502738,0.993417123,35.76301643,1.234209097,2.207106781,0.315300969,0.240676342,1,0.142857143,2,4.414213562,0.630601937,1.558144618,2.288749508,1.504077397,0,1.287854288,0,1.188224447,2.548547388,0.364078198,0.284404899,0.239146312,0.034163759,-0.434935419,5.542451738,0.79177882,0.520591344,8.729166667,0.415674603,2.494047619,2.150389306,0.358398218,12.90233584,2.157972356,3.84667799,0.549525427,0.409560083,2.76515393,0.39502199,4.507402781,7.693355981,1.099050854,3.075489687,3.097117636,2.650135783,2.747408161,4.257378522,4.978206213,6.169089535,2.362463228,0.337494747,0.26135554,0.269137311,0.038448187,-0.399018263,5.542730567,0.791818652,0.520606638,44,2.095238095,12.57142857,12.25,0.560072524,0.093345421,3.360435143,4.343805422,13.06981118,1.867115882,0.781388519,13.06981118,1.867115882,18.30587915,26.13962235,3.734231764,8.638702609,13.0698166,5.379897354,7.621195163,10.31164902,12.83989701,15.4262662,1.946497979,0.27807114,0.20247777,0,0,0,5.80804425,0.829720607,0.534820942,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,36.5,1.303571429,10.35714286,15.44126984,0.681375961,0.11356266,4.088255768,4.007333185,20.97584908,10.73792454,1.53398922,0.721644243,10.73792454,1.53398922,14.59948004,21.33299194,3.047570277,7.524746719,10.73799053,0.405465108,4.968944867,7.049417588,9.517117675,11.86158979,14.24516135,1.930688427,0.275812632,0.199998535,0,0,0,5.813731848,0.830533121,0.535118498,36.518,1.304214286,10.36245714,15.43167983,0.681020103,0.113503351,4.08612062,4.008194868,20.98812135,10.74346067,1.534780096,0.721800939,10.74346067,1.534780096,14.6083039,21.34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compounds,C5H12O4,1850-14-2,74613,PubChem,,14.8,136.17,6.484285714,11.02,21.6108,11.3864,24.3288,0.524761905,1.029085714,0.542209524,1.158514286,0.222222222,21,9,4,20,8,0,8,4,0.2,0,0,0,12,5,0,4,0,0,0,0,0,0,0,0,4,0,57.14285714,23.80952381,0,19.04761905,0,5,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,36,164,210.9421488,51.49637029,220.2,173.2199962,40,120,138.1818182,53.2889259,208,138.427136,6.25,4.916666667,2.5,0.833333333,3,10,3.16227766,4.158883083,1.44,1.587401052,0.49035617,22,2,0.918558654,12,26.52997535,2.645751311,5.708893809,1,30,3.333333333,0.592592593,3.950617284,12,68,12,1.392147224,4,2.444444444,4,2.444444444,320,672,216,824,8.760253966,48,88,8.844155844,3.791656906,1.909772188,3.726000509,0.544444444,0.4,0.133333333,0,2.583333333,1.21875,9.75,7.269222193,23.25,2.583333333,5.860944251,3.424223667,0.018089019,-0.015638901,0.050982707,0.001465168,8.083333333,3.260991176,1.088438073,9.7766E-05,-0.036065955,0.086062034,1.64376E-05,33,1.392147224,2.197224577,3.610917913,4.394449155,5.198497031,5.993961427,6.803505258,7.601402335,8.412054873,9.210440367,10.02131503,2.833213344,0,4.043051268,0,5.549076085,0,7.136483209,0,8.741456116,2.197224577,2.397895273,2.564949357,1.945910149,0,0,0,0,0,0,2.197224577,2.397895273,2.564949357,1.945910149,0,0,0,0,0,0,10.61186811,3.828641396,3.828641396,1,0,2.858095371,2.353691279,7.328427125,4.242640687,2.914213562,3,1.5,0,0.814269681,0.530330086,0.291421356,0.25,0.25,0,6.132993162,2.449489743,1.316496581,1,0.5,0,0.681443685,0.306186218,0.131649658,0.083333333,0.083333333,0,7.328427125,4.242640687,2.914213562,3,1.5,0,29.69848481,1.81575308,36.08166823,2.282657731,3.675765226,1.860521019,2.087629562,92.23866588,29.6094754,1.409975019,1.954685947,5.038310596,1.392147224,3.134866714,70.36869409,443.3713324,1.392147224,3,3,0.333333333,12.52932501,1.934391668,1.36185921,22.03791425,0.998106356,1.316787107,3.633506249,1.409975019,1.58186266,1.58186266,1.58186266,1.372704688,0.917025179,29.6094754,33.21911587,33.21911587,33.21911587,28.82679845,19.25752876,0.321009363,0.360143065,0.360143065,0.360143065,0.312524018,0.208779351,2.982342404,2.810454762,2.810454762,2.810454762,3.019612735,3.475292244,0.3262375,0.366008556,0.366008556,0.366008556,0.317613958,0.212179647,33.9411255,5.236067977,0.581785331,0.647104749,2.236067977,0.248451997,4.472135955,10.47213595,1.163570662,3.891820298,3.03118777,2.679124626,0.297680514,0.385193625,0.149302498,0.016589166,-0.743339633,7.508764405,0.834307156,0.78801163,88,2.444444444,19.55555556,18.5,0.478085525,0.059760691,3.824684203,5.141663557,20.39230485,2.265811649,1.178519683,20.39230485,2.265811649,25.62837282,40.78460969,4.531623299,11.5881628,20.39230485,6.224558429,8.992308733,12.07368917,15.07242518,18.09180596,0,0,0,0,0,0,0,0,0,88,-9.002843951,1.163570662,0,16,1.777777778,1.083707984,5.302775638,0.589197293,4.920809626,12.09153299,1.343503665,7.661997559,5.536838644,3.970291914,5.342334252,6.84694314,8.428799041,10.05298456,3,0.333333333,0.429409129,3,0.333333333,0.429409129,7.589466384,0.843274043,0.792190118,0.942809042,16.97056275,7.884788477,0.98559856,63.07830782,1.475065102,3.121320344,0.346813372,0.444904509,1,0.111111111,2,6.242640687,0.693626743,2.047692843,2.537634471,1.791759469,0,1.504077397,0,1.32175584,2.914213562,0.323801507,0.418069238,0.085786438,0.009531826,-0.95992323,7.52147884,0.835719871,0.788672914,12.20833333,0.33912037,2.712962963,2.730574348,0.341321793,21.84459478,2.442560881,5.264757106,0.584973012,0.649240506,3.052049914,0.339116657,4.851801288,10.52951421,1.169946024,4.055645836,3.413660393,2.922794379,3.058672431,4.63284684,5.49619853,6.742582799,2.606484411,0.289609379,0.374449619,0.111517353,0.012390817,-0.857391792,7.513587222,0.834843025,0.788262599,88,2.444444444,19.55555556,18.5,0.478085525,0.059760691,3.824684203,5.141663557,20.39230485,2.265811649,1.178519683,20.39230485,2.265811649,25.62837282,40.78460969,4.531623299,11.5881628,20.39230485,6.224558429,8.992308733,12.07368917,15.07242518,18.09180596,0,0,0,0,0,0,0,0,0,36,1,8,36,1,0.125,8,3.610917913,8,0.888888889,0.812780988,8,0.888888889,9,16,1.777777778,7.492760301,8.001321867,4.290459441,6.224558429,8.319961022,10.39699406,12.47668358,0,0,0,0,0,0,0,0,0,67,1.488888889,14.77777778,25.66666667,0.631288311,0.078911039,5.050306488,4.658710953,29.75685071,15.37842535,1.708713928,1.068220682,15.37842535,1.708713928,19.23998085,30.53462848,3.392736498,9.466529359,15.37842622,0.693147181,5.651611602,8.149637841,10.94380981,13.66182289,16.39854589,0,0,0,0,0,0,0,0,0,67.0504,1.490008889,14.78924444,25.64454911,0.630803078,0.078850385,5.046424625,4.660018591,29.78331179,15.3904559,1.710050655,1.068526336,15.3904559,1.710050655,19.25529912,30.5592229,3.395469211,9.472311748,15.39045676,0.69194646,5.653199745,8.151972117,10.94694105,13.66573181,16.40323816,0,0,0,0,0,0,0,0,0,121.516,2.700355556,27.18088889,11.62773124,0.344636918,0.043079615,2.757095341,5.732317219,58.39000457,28.39700229,3.155222476,1.307945247,28.39700229,3.15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compounds,C4H10O2,534-15-6,10795,PubChem,,14.2,90.14,5.63375,8.0636,16.0726,8.716,18.4948,0.503975,1.0045375,0.54475,1.155925,0.333333333,16,6,3,15,5,0,5,2,0.133333333,0,0,0,10,4,0,2,0,0,0,0,0,0,0,0,2,0,62.5,25,0,12.5,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,84,107.4710744,25.97780368,107.22,88.749096,19,51,58.27272727,23.65112406,81.11,59.85696,4.333333333,3.666666667,1.666666667,0.777777778,1,5,2.236067977,2.48490665,1.384615385,1.513085749,0.634373395,14,1,1.119150823,4,13.86815329,2.265686062,3.483798383,1,19,3.166666667,0.555555556,2,7,20,6,1.459147917,4,2.066666667,4,2.066666667,113,217,69,205,5.613960566,29,31,5.922077922,3.125244149,2.922077922,3.084656563,0.511111111,0.280952381,0.047619048,0,1.398148148,1.177083333,4.708333333,3.332801014,14.33333333,2.388888889,4.056390715,2.294121481,0.083864727,0.076254273,-0.242655192,0.020603983,7.888888889,2.175445118,0.662501918,0.002188062,0.191363972,-0.429452374,0.001037193,14,1.459147917,1.791759469,3.044522438,3.663561646,4.317488114,4.96284463,5.624017506,6.274762021,6.938284484,7.590346946,8.254528882,2.397895273,0,3.433987204,0,4.672828834,0,5.96870756,0,7.280008253,1.791759469,1.791759469,1.609437912,0.693147181,0,0,0,0,0,0,1.791759469,1.791759469,1.609437912,0.693147181,0,0,0,0,0,0,8.980172206,3.091042453,3.091042453,1,0,2.457522674,2.031359649,4.991563832,2.808060412,1.921668296,1.39384685,0.288675135,0,0.831927305,0.561612082,0.384333659,0.348461713,0.288675135,0,4.39384685,1.865251371,1.039034086,0.663854611,0.096225045,0,0.732307808,0.373050274,0.207806817,0.165963653,0.096225045,0,4.991563832,2.808060412,1.921668296,1.39384685,0.288675135,0,19.07907262,1.558573039,16.24541588,1.777533865,2.4864189,1.42920136,1.614682216,64,20.78071905,1.298794941,1.825605199,3.760017278,1.918295834,2.55136865,27.38407799,116.5605356,1.459147917,2.446439345,4,0.666666667,11.509775,1.952882292,1.16584986,11.29895826,0.954772093,1.244055344,3.135157771,1.298794941,1.673794941,2.483458593,2.483458593,2.233458593,1.358458593,20.78071905,26.78071905,39.73533749,39.73533749,35.73533749,21.73533749,0.324698735,0.418448735,0.620864648,0.620864648,0.558364648,0.339614648,2.701205059,2.326205059,1.516541407,1.516541407,1.766541407,2.641541407,0.332436988,0.428421247,0.635661156,0.635661156,0.57167165,0.347708378,14.04030206,3.449489743,0.574914957,0.322652915,1.931851653,0.321975275,3.863703305,6.898979486,1.149829914,2.564949357,2.595799778,2.300795291,0.383465882,0.217126788,0.125467544,0.020911257,-0.540881162,4.585015974,0.764169329,0.396804512,31,2.066666667,10.33333333,9.083333333,0.550836957,0.110167391,2.754184786,4.007333185,10.74240473,1.790400788,0.618660906,10.74240473,1.790400788,15.97847271,21.48480946,3.580801576,7.28276118,10.74245678,5.043425117,6.974478911,9.557257866,11.84151673,14.2589257,1.946497979,0.32441633,0.173552374,0,0,0,4.80804425,0.801340708,0.409181074,31,-4.909237255,1.745355992,0,10,1.666666667,0.6,4.302775638,0.717129273,3.920809626,7.841619253,1.306936542,4.912654886,4.609916671,3.433987204,4.672828834,6.008813185,7.396948603,8.814776089,2.449489743,0.40824829,0.233445375,2.449489743,0.40824829,0.233445375,4.629100499,0.77151675,0.399297966,0.936020137,9.459457418,4.97084553,0.994169106,24.85422765,1.174759276,2.115355072,0.352559179,0.195229966,1,0.166666667,2,4.230710143,0.705118357,1.504077397,2.172059387,1.466337069,0,1.268511325,0,1.181499948,2.390833047,0.398472174,0.22712955,0.030476867,0.005079478,-0.909617804,4.587721209,0.764620201,0.396958211,6.756944444,0.450462963,2.252314815,2.067972932,0.413594586,10.33986466,1.954638617,3.370497738,0.561749623,0.316616424,2.434614161,0.405769027,4.056377733,6.740995476,1.123499246,2.764662539,2.819372086,2.462284175,2.310108802,3.802264817,4.305323589,5.43398345,2.25952424,0.376587373,0.212410203,0.09494586,0.01582431,-0.613514381,4.588128707,0.764688118,0.396981355,31,2.066666667,10.33333333,9.083333333,0.550836957,0.110167391,2.754184786,4.007333185,10.74240473,1.790400788,0.618660906,10.74240473,1.790400788,15.97847271,21.48480946,3.580801576,7.28276118,10.74245678,5.043425117,6.974478911,9.557257866,11.84151673,14.2589257,1.946497979,0.32441633,0.173552374,0,0,0,4.80804425,0.801340708,0.409181074,15,1,5,15,1,0.2,5,2.772588722,5,0.833333333,0.419382003,5,0.833333333,6,10,1.666666667,4.875197323,5.018950998,3.433987204,4.795790546,6.447305863,8.045908742,9.657011401,0,0,0,0,0,0,0,0,0,25,1.19047619,8.25,11.9984127,0.689954014,0.137990803,3.449770068,3.622672876,16.61577351,8.557886757,1.42631446,0.559419921,8.557886757,1.42631446,12.41944226,16.94910685,2.824851141,6.146463082,8.558442195,0.405465108,4.570837507,6.316769753,8.635099831,10.71332746,12.89010543,1.930688427,0.321781404,0.171427316,0,0,0,4.813731848,0.802288641,0.409489136,25.0144,1.191161905,8.255,11.98959821,0.689534792,0.137906958,3.447673962,3.623674342,16.62739473,8.563097367,1.427182894,0.559578529,8.563097367,1.427182894,12.4279406,16.95992807,2.826654678,6.149521172,8.5636496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+UMLYSI0441,L,ethyl (E)-2-methylbut-2-enoate,CCOC(=O)/C(=C/C)/C,unsaturated 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amine,C4H11N,75-64-9,6385,PubChem,,21.1,73.16,4.5725,7.6554,15.5198,8.8127,18.5743,0.4784625,0.9699875,0.55079375,1.16089375,0.4,16,5,4,15,4,0,4,0,0,0,0,0,11,4,1,0,0,0,0,0,0,0,0,0,1,0,68.75,25,6.25,0,0,4,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,20,28,29.31223963,21.35995469,38.6,33.44240009,16,24,24.84507042,16.66611157,34.96,30.40228,4.25,3.583333333,1,0.833333333,3,6,2.449489743,1.386294361,1.176470588,1.319507911,0.8493218,10.5,2,1.341506351,0,9.169934957,1.673320053,2.884053773,1,9,1.8,0.32,0.96,4,12,3,0.721928095,2,1.6,2,1.6,60,82,28,48,3.972356867,12,16,4.961038961,0.977357471,3.961038961,0.969741699,0.6,0,0,0,1.25,1.354166667,2.708333333,4.481741253,11.25,2.25,3.515747535,2.344580192,0.146792719,-0.116049127,0.324982901,0.074489542,7.75,1.827921234,0.597610051,0.006325243,-0.316253916,0.655083456,0.005296794,17,0.721928095,1.609437912,3.044522438,3.496507561,4.394449155,4.859812404,5.771441123,6.240275845,7.155396302,7.625107148,8.541105011,2.197224577,0,3.496507561,0,4.859812404,0,6.240275845,0,7.625107148,1.609437912,1.945910149,0,0,0,0,0,0,0,0,1.609437912,1.945910149,0,0,0,0,0,0,0,0,9.164296433,2.772588722,2.772588722,1,0,2.251291799,1.941646022,4.5,2,3,0,0,0,0.9,0.5,0.5,0,0,0,4.077350269,1.788675135,2.366025404,0,0,0,0.815470054,0.447168784,0.394337567,0,0,0,4.5,2,3,0,0,0,12.25,1.264911064,12.64911064,1.779017701,1.959624428,1.435026478,1.538836749,64,17.94625219,1.121640762,0.970950594,3.25,0.721928095,2.293564443,9.709505945,52,0.721928095,2,3,0.6,6.854752972,1.241146095,1.083984339,8.101474426,1.552537557,1.531034756,0,1.121640762,1.794736718,1.794736718,1.544736718,0.841917187,0,17.94625219,28.71578748,28.71578748,24.71578748,13.47067499,0,0.280410191,0.448684179,0.448684179,0.386184179,0.210479297,0,2.878359238,2.205263282,2.205263282,2.455263282,3.158082813,0,0.287092954,0.459377265,0.459377265,0.395387759,0.21549546,0,8,2,0.4,0.150514998,2,0.4,4,4,0.8,1.791759469,2.444465779,2.121320344,0.424264069,0.163303128,0.707106781,0.141421356,-0.075257499,3.577708764,0.715541753,0.276802492,16,1.6,6.4,7,0.755928946,0.188982237,3.023715784,3.135494216,6.605551275,1.321110255,0.409954534,6.605551275,1.321110255,8.605551275,13.21110255,2.64222051,5.505331536,6.608188235,4.043051268,5.579729826,7.577121931,9.431962767,11.32978357,0,0,0,0,0,0,0,0,0,16,-3.101120416,0.8,0,8,1.6,0.451544993,5,1,4,6.8,1.36,3.891820298,5.066183552,3.36729583,4.859812404,6.444131257,8.048468744,9.656883442,2.236067977,0.447213595,0.174742501,2.236067977,0.447213595,0.174742501,3.651483717,0.730296743,0.281234684,0.8,8,4,1,16,0.916290732,1,0.2,0,1,0.2,2,2,0.4,1.098612289,1.958143765,1.098612289,0,1.098612289,0,1.098612289,2.121320344,0.424264069,0.163303128,0.707106781,0.141421356,-0.075257499,3.577708764,0.715541753,0.276802492,5.5,0.55,2.2,1.511857892,0.377964473,6.047431568,1.781288169,2.409852575,0.481970515,0.190995237,2.409852575,0.481970515,4.069705149,4.819705149,0.96394103,2.33449247,2.685046923,2.277267285,2.339399066,3.745451759,4.243796721,5.383450761,2.046780782,0.409356156,0.155535665,0.507762566,0.101552513,-0.14716966,3.583852385,0.716770477,0.277175057,16,1.6,6.4,7,0.755928946,0.188982237,3.023715784,3.135494216,6.605551275,1.321110255,0.409954534,6.605551275,1.321110255,8.605551275,13.21110255,2.64222051,5.505331536,6.608188235,4.043051268,5.579729826,7.577121931,9.431962767,11.32978357,0,0,0,0,0,0,0,0,0,10,1,4,10,1,0.25,4,2.397895273,4,0.8,0.301029996,4,0.8,5,8,1.6,3.931825633,4.044272764,3.044522438,4.110873864,5.564520407,6.928537818,8.318986125,0,0,0,0,0,0,0,0,0,15.57142857,1.038095238,6.2,7.424908425,0.781959857,0.195489964,3.127839429,3.083593629,12.66410847,6.403482805,1.280696561,0.403208124,6.403482805,1.280696561,8.403482805,12.74982275,2.54996455,5.340085852,6.406821759,0.133531393,3.97375146,5.491284199,7.45160055,9.277051553,11.14325785,0,0,0,0,0,0,0,0,0,15.5716,1.038106667,6.20008,7.424723807,0.781949044,0.195487261,3.127796175,3.083614003,12.66432631,6.403563153,1.280712631,0.403210848,6.403563153,1.280712631,8.403563153,12.75000631,2.550001261,5.340155609,6.406901786,0.133481391,3.973778781,5.491320659,7.451650882,9.277114286,11.14333311,0,0,0,0,0,0,0,0,0,16.9576,1.130506667,6.84688,6.232384673,0.704137384,0.176034346,2.816549534,3.256861992,14.44776271,7.064281355,1.412856271,0.424533994,7.064281355,1.412856271,9.811141375,14.25624271,2.851248542,5.824005159,7.065873904,-0.277025492,4.202520078,5.757489631,7.855742803,9.76270228,11.73479248,1.819539737,0.363907947,0.129980772,0.087221662,0.017444332,-0.529687821,3.911388979,0.782277796,0.296165504,15.5863,1.039086667,6.20694,7.408942501,0.781023033,0.195255758,3.124092131,3.085362308,12.68300905,6.410454526,1.282090905,0.403444412,6.410454526,1.282090905,8.410454526,12.76574905,2.553149811,5.346125554,6.413765763,0.129184458,3.97612295,5.494443975,7.455966481,9.28249143,11.1497849,0,0,0,0,0,0,0,0,0,17.8,1.186666667,7.24,5.678846154,0.664556222,0.166139055,2.658224888,3.36729583,15.54821161,7.474105805,1.494821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amine,C6H15N,13360-63-9,25914,PubChem,,22.1,101.22,4.600909091,10.709,21.287,12.3355,25.4047,0.486772727,0.967590909,0.560704545,1.154759091,0.285714286,22,7,2,21,6,0,6,4,0.19047619,0,0,0,15,6,1,0,0,0,0,0,0,0,0,0,1,0,68.18181818,27.27272727,4.545454545,0,0,6,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,22,34,36.33287046,23.38768623,46.3,35.9316,20,28,29.12676056,21.16569525,39.96,29.8808,4.5,4.25,2,1.75,0,5,2.236067977,3.465735903,1.555555556,1.640670712,0.537158271,14.5,0,1.560660172,4,19.24902768,3.055050463,3.627465013,1,33,4.714285714,0.897959184,2.857142857,12,28,9,1.950212065,6,2.666666667,6,2.666666667,202,236,146,186,7.297142506,54,56,6.961038961,5.961038961,5.961038961,5.927860637,0.452380952,0.219387755,0.096598639,0.042857143,0.388888889,0.749166667,2.178888889,6.729604658,12.5,1.785714286,5.312655418,3.520829431,0.131506809,-0.030812547,0.116811031,0.023809524,7.285714286,2.602480803,0.836211705,0.000973526,-0.108345305,0.250205155,0.000333268,13,1.950212065,1.945910149,3.135494216,3.713572067,4.317488114,4.919980926,5.533389489,6.142037406,6.758094504,7.368339686,7.985143931,2.564949357,0,3.496507561,0,4.574710979,0,5.720311777,0,6.900730664,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,1.945910149,1.791759469,1.609437912,1.386294361,1.098612289,0.693147181,0,0,0,0,8.761236807,3.36729583,3.36729583,1,0,2.627114062,2.132338507,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,0.790790558,0.569035594,0.412132034,0.301776695,0.225592232,0.176776695,5.328427125,3.121320344,1.707106781,0.957106781,0.478553391,0.25,0.761203875,0.520220057,0.341421356,0.239276695,0.159517797,0.125,5.535533906,3.414213562,2.060660172,1.207106781,0.676776695,0.353553391,20.98528137,1.840004522,19.78207923,3.097664558,3.327694298,2.602395727,2.952807591,98.10749561,23.99436167,1.090652803,2.398302995,4.168986958,1.950212065,2.774244972,50.3643629,233.4632697,0.863120569,2.751629167,7,1,19.65148445,2.440007983,1.22222776,15.01828953,0.666081376,1.019498601,1.729713704,1.090652803,1.763842123,3.027169118,3.663532755,3.663532755,3.460831318,23.99436167,38.8045267,66.59772061,80.59772061,80.59772061,76.13828899,0.244572155,0.395530703,0.67882398,0.821524595,0.821524595,0.77607005,3.368778815,2.695589496,1.4322625,0.795898864,0.795898864,0.998600301,0.248309195,0.401574375,0.689196346,0.834077413,0.834077413,0.787928327,20.48528137,4.027339492,0.575334213,0.423512769,1.847759065,0.263965581,3.69551813,8.054678984,1.150668426,3.091042453,2.73919265,2.513669746,0.359095678,0.280215753,0.099456184,0.014208026,-0.701657747,5.592530245,0.798932892,0.523325839,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,56,-7.498040439,2.885095623,0,12,1.714285714,0.755426872,3.801937736,0.543133962,3.603875472,8.702204129,1.243172018,5.934894196,4.522970604,3.555348061,4.691347882,5.894402834,7.133295955,8.394121194,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.975489589,10.82842712,5.985143843,0.997523974,35.91086306,1.310251729,2.366025404,0.338003629,0.261813582,1,0.142857143,2,4.732050808,0.676007258,1.681758574,2.316436311,1.609437912,0,1.446918983,0,1.354545663,2.618591722,0.374084532,0.292647453,0.169101979,0.024157426,-0.540295917,5.594918316,0.799274045,0.523455625,7.989722222,0.380462963,2.282777778,2.564019422,0.42733657,15.38411653,2.102137974,4.119483043,0.588497578,0.430389904,2.400399559,0.342914223,3.963351092,8.238966087,1.176995155,3.346584212,2.935808684,2.621413019,2.328341703,3.846629402,4.257734494,5.383121673,2.490590677,0.355798668,0.277411651,0.067967869,0.009709696,-0.817387444,5.593895954,0.799127993,0.523400069,56,2.666666667,16,11.15,0.407912148,0.067985358,2.447472888,4.844187086,16.6253823,2.375054615,0.854540149,16.6253823,2.375054615,26.72321698,33.2507646,4.750109229,8.945071894,16.62538247,5.973809612,8.168769824,11.3726629,13.96806081,16.91462223,2.341896018,0.334556574,0.258697326,0,0,0,5.721964274,0.817423468,0.530281599,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,53,1.892857143,15.12244898,11.95358555,0.432700019,0.07211667,2.596200114,4.744044423,31.33744926,15.7401532,2.248593314,0.837906269,15.7401532,2.248593314,25.20200528,31.43949008,4.491355725,8.651145924,15.74015364,0.133531393,5.859564476,8.013356692,11.14898088,13.69900254,16.58345192,2.370603005,0.338657572,0.262401181,0.035061232,0.005008747,-1.018620979,5.716223251,0.816603322,0.529976427,53.0012,1.8929,15.1228,11.95322862,0.432689477,0.072114913,2.59613686,4.744085169,31.3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nitrate,CC(C)O[N+](=O)[O-],,C3H7NO3,1712-64-7,15575,PubChem,,1.05,105.11,7.507857143,7.7462,14.7345,7.6534,16.3721,0.5533,1.052464286,0.546671429,1.169435714,0.285714286,14,7,4,13,6,2,8,2,0.153846154,2,0,0,7,3,1,3,0,0,0,0,0,0,0,0,4,0,50,21.42857143,7.142857143,21.42857143,0,3,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,26,144,182.8517217,36.4924955,194.8,161.945192,24,114,137.6619718,31.86714929,170.36,126.1889,5.166666667,3.033333333,1.666666667,0.822222222,2,7,2.645751311,2.890371758,1.35483871,1.511209391,0.588197464,17.5,2,0.969062678,4,18.18873053,2.526054707,4.585845777,1,24,3.428571429,0.653061224,2.612244898,10,26,6,1.378783493,4,2.285714286,4,2.285714286,174,486,114,620,6.786218125,38,48,6.532467532,2.663383824,5.532467532,2.48549548,0.547619048,0.19047619,0.095238095,0,2.800925926,2.393055556,10.51666667,7.929271133,24.83333333,3.547619048,4.277797177,2.600141878,0.023111352,-0.05868948,0.157542516,0.006465082,9.047619048,2.494612094,0.861697779,0.000622869,-0.115325994,0.254704197,0.000288581,21,1.378783493,1.945910149,3.295836866,3.891820298,4.65396035,5.262690189,6.033086222,6.64509097,7.417580402,8.030409562,8.803424212,2.564949357,0,3.713572067,0,4.976733742,0,6.300785795,0,7.655864018,1.945910149,2.079441542,1.609437912,1.609437912,0,0,0,0,0,0,2.197224577,2.564949357,1.945910149,2.197224577,0,0,0,0,0,0,9.46962297,3.36729583,3.737669618,1.109991461,0.370373788,2.603368154,2.145270715,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,0.837401046,0.520982943,0.431913709,0.23570226,0.23570226,0,4.249308736,1.938125356,1.377770843,0.296884807,0.172132593,0,0.607044105,0.323020893,0.196824406,0.074221202,0.043033148,0,5.86180732,3.125897658,3.023395961,0.942809042,0.942809042,0,21.50805748,1.654068949,21.90865123,1.86769148,2.733902054,1.544452153,1.764348752,53.30296891,24.14170945,1.724407818,1.956066972,4.230388542,1.378783493,2.779475245,41.07740642,203.05865,1.378783493,2.625814584,5,0.714285714,15.65148445,2.001032983,1.23863892,14.72926917,0.875221883,1.20739461,2.67238404,1.724407818,2.117963175,2.413799565,2.413799565,1.869891719,0.924933169,24.14170945,29.65148445,33.7931939,33.7931939,26.17848406,12.94906437,0.452914912,0.556282043,0.633983333,0.633983333,0.49112619,0.242933267,2.082947104,1.689391747,1.393555357,1.393555357,1.937463203,2.882421753,0.441376019,0.542109671,0.617831369,0.617831369,0.478613791,0.236744067,18.75538595,3.414213562,0.487744795,0.373303478,2,0.285714286,4,6.828427125,0.975489589,2.772588722,2.765108219,2.5,0.357142857,0.278558006,0.5,0.071428571,-0.210720997,5.560090548,0.79429865,0.521557305,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,48,-6.426891805,2.132600461,0,12,1.714285714,0.755426872,4.414213562,0.630601937,4.14626437,9.435385883,1.347912269,5.82008293,4.927315195,3.663561646,4.976733742,6.368187186,7.788626066,9.222170124,2.645751311,0.377964473,0.295784314,2.645751311,0.377964473,0.295784314,5.61248608,0.801783726,0.524408694,0.893113616,11.82718272,5.981268914,0.996878152,35.88761348,1.222681352,1.816496581,0.259499512,0.181464209,1,0.142857143,2,3.632993162,0.518999023,1.466337069,2.28143274,1.466337069,0,1.358123484,0,1.278874112,2.562948829,0.366135547,0.286117955,0.562948829,0.080421261,-0.174671756,5.561848899,0.794549843,0.52165343,8.444444444,0.402116402,2.412698413,2.248851359,0.37480856,13.49310816,2.136940824,3.815288462,0.545041209,0.407069165,2.606803347,0.372400478,4.261858866,7.630576925,1.090082418,3.282657138,3.027112858,2.639415511,2.603311063,4.094262329,4.675754999,5.840902485,2.447753146,0.349679021,0.272137332,0.351391722,0.050198817,-0.317945932,5.563998554,0.794856936,0.521770906,48,2.285714286,13.71428571,11.83333333,0.492203862,0.082033977,2.953223175,4.521788577,14.17597107,2.025138724,0.806086973,14.17597107,2.025138724,21.64007268,28.35194213,4.050277447,8.732949524,14.17597291,5.59471138,7.788626066,10.68118266,13.2162926,15.93039272,2.426262835,0.346608976,0.269456492,0,0,0,5.739236321,0.819890903,0.531197875,21,1,6,21,1,0.166666667,6,3.091042453,6,0.857142857,0.544705875,6,0.857142857,7,12,1.714285714,5.777652323,6.007918609,3.761200116,5.351858133,7.172424577,8.958154135,10.75070684,0,0,0,0,0,0,0,0,0,33.46428571,1.195153061,9.433673469,21.47057552,0.715514385,0.119252397,4.293086308,3.911933716,18.75597998,9.82441856,1.403488366,0.6946148,9.82441856,1.403488366,15.02978845,19.39373508,2.770533583,6.428408028,9.824628952,0.638087403,4.86634654,6.683544171,9.217129853,11.3813372,13.73118064,2.510938117,0.358705445,0.279885207,0.240356358,0.034336623,-0.43340107,5.713952646,0.816278949,0.529855646,33.47965776,1.195702063,9.438330789,21.454627,0.715126429,0.119187738,4.290758572,3.9127325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new file mode 100644
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By,Notes,TARGET,nAcid,ALogP,ALogp2,AMR,apol,naAromAtom,nAromBond,nAtom,nHeavyAtom,nH,nB,nC,nN,nO,nS,nP,nF,nCl,nBr,nI,nX,ATS0m,ATS1m,ATS2m,ATS3m,ATS4m,ATS5m,ATS6m,ATS7m,ATS8m,ATS0v,ATS1v,ATS2v,ATS3v,ATS4v,ATS5v,ATS6v,ATS7v,ATS8v,ATS0e,ATS1e,ATS2e,ATS3e,ATS4e,ATS5e,ATS6e,ATS7e,ATS8e,ATS0p,ATS1p,ATS2p,ATS3p,ATS4p,ATS5p,ATS6p,ATS7p,ATS8p,ATS0i,ATS1i,ATS2i,ATS3i,ATS4i,ATS5i,ATS6i,ATS7i,ATS8i,ATS0s,ATS1s,ATS2s,ATS3s,ATS4s,ATS5s,ATS6s,ATS7s,ATS8s,AATS0m,AATS1m,AATS2m,AATS3m,AATS4m,AATS5m,AATS6m,AATS7m,AATS8m,AATS0v,AATS1v,AATS2v,AATS3v,AATS4v,AATS5v,AATS6v,AATS7v,AATS8v,AATS0e,AATS1e,AATS2e,AATS3e,AATS4e,AATS5e,AATS6e,AATS7e,AATS8e,AATS0p,AATS1p,AATS2p,AATS3p,AATS4p,AATS5p,AATS6p,AATS7p,AATS8p,AATS0i,AATS1i,AATS2i,AATS3i,AATS4i,AATS5i,AATS6i,AATS7i,AATS8i,AATS0s,AATS1s,AATS2s,AATS3s,AATS4s,AATS5s,AATS6s,AATS7s,AATS8s,ATSC0c,ATSC1c,ATSC2c,ATSC3c,ATSC4c,ATSC5c,ATSC6c,ATSC7c,ATSC8c,ATSC0m,ATSC1m,ATSC2m,ATSC3m,ATSC4m,ATSC5m,ATSC6m,ATSC7m,ATSC8m,ATSC0v,ATSC1v,ATSC2v,ATSC3v,ATSC4v,ATSC5v,ATSC6v,ATSC7v,ATSC8v,ATSC0e,ATSC1e,ATSC2e,ATSC3e,ATSC4e,ATSC5e,ATSC6e,ATSC7e,ATSC8e,ATSC0p,ATSC1p,ATSC2p,ATSC3p,ATSC4p,ATSC5p,ATSC6p,ATSC7p,ATSC8p,ATSC0i,ATSC1i,ATSC2i,ATSC3i,ATSC4i,ATSC5i,ATSC6i,ATSC7i,ATSC8i,ATSC0s,ATSC1s,ATSC2s,ATSC3s,ATSC4s,ATSC5s,ATSC6s,ATSC7s,ATSC8s,AATSC0c,AATSC1c,AATSC2c,AATSC3c,AATSC4c,AATSC5c,AATSC6c,AATSC7c,AATSC8c,AATSC0m,AATSC1m,AATSC2m,AATSC3m,AATSC4m,AATSC5m,AATSC6m,AATSC7m,AATSC8m,AATSC0v,AATSC1v,AATSC2v,AATSC3v,AATSC4v,AATSC5v,AATSC6v,AATSC7v,AATSC8v,AATSC0e,AATSC1e,AATSC2e,AATSC3e,AATSC4e,AATSC5e,AATSC6e,AATSC7e,AATSC8e,AATSC0p,AATSC1p,AATSC2p,AATSC3p,AATSC4p,AATSC5p,AATSC6p,AATSC7p,AATSC8p,AATSC0i,AATSC1i,AATSC2i,AATSC3i,AATSC4i,AATSC5i,AATSC6i,AATSC7i,AATSC8i,AATSC0s,AATSC1s,AATSC2s,AATSC3s,AATSC4s,AATSC5s,AATSC6s,AATSC7s,AATSC8s,MATS1c,MATS2c,MATS3c,MATS4c,MATS5c,MATS6c,MATS7c,MATS8c,MATS1m,MATS2m,MATS3m,MATS4m,MATS5m,MATS6m,MATS7m,MATS8m,MATS1v,MATS2v,MATS3v,MATS4v,MATS5v,MATS6v,MATS7v,MATS8v,MATS1e,MATS2e,MATS3e,MATS4e,MATS5e,MATS6e,MATS7e,MATS8e,MATS1p,MATS2p,MATS3p,MATS4p,MATS5p,MATS6p,MATS7p,MATS8p,MATS1i,MATS2i,MATS3i,MATS4i,MATS5i,MATS6i,MATS7i,MATS8i,MATS1s,MATS2s,MATS3s,MATS4s,MATS5s,MATS6s,MATS7s,MATS8s,GATS1c,GATS2c,GATS3c,GATS4c,GATS5c,GATS6c,GATS7c,GATS8c,GATS1m,GATS2m,GATS3m,GATS4m,GATS5m,GATS6m,GATS7m,GATS8m,GATS1v,GATS2v,GATS3v,GATS4v,GATS5v,GATS6v,GATS7v,GATS8v,GATS1e,GATS2e,GATS3e,GATS4e,GATS5e,GATS6e,GATS7e,GATS8e,GATS1p,GATS2p,GATS3p,GATS4p,GATS5p,GATS6p,GATS7p,GATS8p,GATS1i,GATS2i,GATS3i,GATS4i,GATS5i,GATS6i,GATS7i,GATS8i,GATS1s,GATS2s,GATS3s,GATS4s,GATS5s,GATS6s,GATS7s,GATS8s,SpAbs_DzZ,SpMax_DzZ,SpDiam_DzZ,SpAD_DzZ,SpMAD_DzZ,EE_DzZ,SM1_DzZ,VE1_DzZ,VE2_DzZ,VE3_DzZ,VR1_DzZ,VR2_DzZ,VR3_DzZ,SpAbs_Dzm,SpMax_Dzm,SpDiam_Dzm,SpAD_Dzm,SpMAD_Dzm,EE_Dzm,SM1_Dzm,VE1_Dzm,VE2_Dzm,VE3_Dzm,VR1_Dzm,VR2_Dzm,VR3_Dzm,SpAbs_Dzv,SpMax_Dzv,SpDiam_Dzv,SpAD_Dzv,SpMAD_Dzv,EE_Dzv,SM1_Dzv,VE1_Dzv,VE2_Dzv,VE3_Dzv,VR1_Dzv,VR2_Dzv,VR3_Dzv,SpAbs_Dze,SpMax_Dze,SpDiam_Dze,SpAD_Dze,SpMAD_Dze,EE_Dze,SM1_Dze,VE1_Dze,VE2_Dze,VE3_Dze,VR1_Dze,VR2_Dze,VR3_Dze,SpAbs_Dzp,SpMax_Dzp,SpDiam_Dzp,SpAD_Dzp,SpMAD_Dzp,EE_Dzp,SM1_Dzp,VE1_Dzp,VE2_Dzp,VE3_Dzp,VR1_Dzp,VR2_Dzp,VR3_Dzp,SpAbs_Dzi,SpMax_Dzi,SpDiam_Dzi,SpAD_Dzi,SpMAD_Dzi,EE_Dzi,SM1_Dzi,VE1_Dzi,VE2_Dzi,VE3_Dzi,VR1_Dzi,VR2_Dzi,VR3_Dzi,SpAbs_Dzs,SpMax_Dzs,SpDiam_Dzs,SpAD_Dzs,SpMAD_Dzs,EE_Dzs,SM1_Dzs,VE1_Dzs,VE2_Dzs,VE3_Dzs,VR1_Dzs,VR2_Dzs,VR3_Dzs,nBase,BCUTw-1l,BCUTw-1h,BCUTc-1l,BCUTc-1h,BCUTp-1l,BCUTp-1h,nBonds,nBonds2,nBondsS,nBondsS2,nBondsS3,nBondsD,nBondsD2,nBondsT,nBondsQ,nBondsM,bpol,SpMax1_Bhm,SpMax2_Bhm,SpMax3_Bhm,SpMax4_Bhm,SpMax5_Bhm,SpMax6_Bhm,SpMax7_Bhm,SpMax8_Bhm,SpMin1_Bhm,SpMin2_Bhm,SpMin3_Bhm,SpMin4_Bhm,SpMin5_Bhm,SpMin6_Bhm,SpMin7_Bhm,SpMin8_Bhm,SpMax1_Bhv,SpMax2_Bhv,SpMax3_Bhv,SpMax4_Bhv,SpMax5_Bhv,SpMax6_Bhv,SpMax7_Bhv,SpMax8_Bhv,SpMin1_Bhv,SpMin2_Bhv,SpMin3_Bhv,SpMin4_Bhv,SpMin5_Bhv,SpMin6_Bhv,SpMin7_Bhv,SpMin8_Bhv,SpMax1_Bhe,SpMax2_Bhe,SpMax3_Bhe,SpMax4_Bhe,SpMax5_Bhe,SpMax6_Bhe,SpMax7_Bhe,SpMax8_Bhe,SpMin1_Bhe,SpMin2_Bhe,SpMin3_Bhe,SpMin4_Bhe,SpMin5_Bhe,SpMin6_Bhe,SpMin7_Bhe,SpMin8_Bhe,SpMax1_Bhp,SpMax2_Bhp,SpMax3_Bhp,SpMax4_Bhp,SpMax5_Bhp,SpMax6_Bhp,SpMax7_Bhp,SpMax8_Bhp,SpMin1_Bhp,SpMin2_Bhp,SpMin3_Bhp,SpMin4_Bhp,SpMin5_Bhp,SpMin6_Bhp,SpMin7_Bhp,SpMin8_Bhp,SpMax1_Bhi,SpMax2_Bhi,SpMax3_Bhi,SpMax4_Bhi,SpMax5_Bhi,SpMax6_Bhi,SpMax7_Bhi,SpMax8_Bhi,SpMin1_Bhi,SpMin2_Bhi,SpMin3_Bhi,SpMin4_Bhi,SpMin5_Bhi,SpMin6_Bhi,SpMin7_Bhi,SpMin8_Bhi,SpMax1_Bhs,SpMax2_Bhs,SpMax3_Bhs,SpMax4_Bhs,SpMax5_Bhs,SpMax6_Bhs,SpMax7_Bhs,SpMax8_Bhs,SpMin1_Bhs,SpMin2_Bhs,SpMin3_Bhs,SpMin4_Bhs,SpMin5_Bhs,SpMin6_Bhs,SpMin7_Bhs,SpMin8_Bhs,C1SP1,C2SP1,C1SP2,C2SP2,C3SP2,C1SP3,C2SP3,C3SP3,C4SP3,SCH-3,SCH-4,SCH-5,SCH-6,SCH-7,VCH-3,VCH-4,VCH-5,VCH-6,VCH-7,SC-3,SC-4,SC-5,SC-6,VC-3,VC-4,VC-5,VC-6,SPC-4,SPC-5,SPC-6,VPC-4,VPC-5,VPC-6,SP-0,SP-1,SP-2,SP-3,SP-4,SP-5,SP-6,SP-7,ASP-0,ASP-1,ASP-2,ASP-3,ASP-4,ASP-5,ASP-6,ASP-7,VP-0,VP-1,VP-2,VP-3,VP-4,VP-5,VP-6,VP-7,AVP-0,AVP-1,AVP-2,AVP-3,AVP-4,AVP-5,AVP-6,AVP-7,Sv,Sse,Spe,Sare,Sp,Si,Mv,Mse,Mpe,Mare,Mp,Mi,CrippenLogP,CrippenMR,SpMax_Dt,SpDiam_Dt,SpAD_Dt,SpMAD_Dt,EE_Dt,VE1_Dt,VE2_Dt,VE3_Dt,VR1_Dt,VR2_Dt,VR3_Dt,ECCEN,nHBd,nwHBd,nHBa,nwHBa,nHBint2,nHBint3,nHBint4,nHBint5,nHBint6,nHBint7,nHBint8,nHBint9,nHBint10,nHsOH,nHdNH,nHsSH,nHsNH2,nHssNH,nHaaNH,nHsNH3p,nHssNH2p,nHsssNHp,nHtCH,nHdCH2,nHdsCH,nHaaCH,nHCHnX,nHCsats,nHCsatu,nHAvin,nHother,nHmisc,nsLi,nssBe,nssssBem,nsBH2,nssBH,nsssB,nssssBm,nsCH3,ndCH2,nssCH2,ntCH,ndsCH,naaCH,nsssCH,nddC,ntsC,ndssC,naasC,naaaC,nssssC,nsNH3p,nsNH2,nssNH2p,ndNH,nssNH,naaNH,ntN,nsssNHp,ndsN,naaN,nsssN,nddsN,naasN,nssssNp,nsOH,ndO,nssO,naaO,naOm,nsOm,nsF,nsSiH3,nssSiH2,nsssSiH,nssssSi,nsPH2,nssPH,nsssP,ndsssP,nddsP,nsssssP,nsSH,ndS,nssS,naaS,ndssS,nddssS,nssssssS,nSm,nsCl,nsGeH3,nssGeH2,nsssGeH,nssssGe,nsAsH2,nssAsH,nsssAs,ndsssAs,nddsAs,nsssssAs,nsSeH,ndSe,nssSe,naaSe,ndssSe,nssssssSe,nddssSe,nsBr,nsSnH3,nssSnH2,nsssSnH,nssssSn,nsI,nsPbH3,nssPbH2,nsssPbH,nssssPb,SHBd,SwHBd,SHBa,SwHBa,SHBint2,SHBint3,SHBint4,SHBint5,SHBint6,SHBint7,SHBint8,SHBint9,SHBint10,SHsOH,SHdNH,SHsSH,SHsNH2,SHssNH,SHaaNH,SHsNH3p,SHssNH2p,SHsssNHp,SHtCH,SHdCH2,SHdsCH,SHaaCH,SHCHnX,SHCsats,SHCsatu,SHAvin,SHother,SHmisc,SsLi,SssBe,SssssBem,SsBH2,SssBH,SsssB,SssssBm,SsCH3,SdCH2,SssCH2,StCH,SdsCH,SaaCH,SsssCH,SddC,StsC,SdssC,SaasC,SaaaC,SssssC,SsNH3p,SsNH2,SssNH2p,SdNH,SssNH,SaaNH,StN,SsssNHp,SdsN,SaaN,SsssN,SddsN,SaasN,SssssNp,SsOH,SdO,SssO,SaaO,SaOm,SsOm,SsF,SsSiH3,SssSiH2,SsssSiH,SssssSi,SsPH2,SssPH,SsssP,SdsssP,SddsP,SsssssP,SsSH,SdS,SssS,SaaS,SdssS,SddssS,SssssssS,SSm,SsCl,SsGeH3,SssGeH2,SsssGeH,SssssGe,SsAsH2,SssAsH,SsssAs,SdsssAs,SddsAs,SsssssAs,SsSeH,SdSe,SssSe,SaaSe,SdssSe,SssssssSe,SddssSe,SsBr,SsSnH3,SssSnH2,SsssSnH,SssssSn,SsI,SsPbH3,SssPbH2,SsssPbH,SssssPb,minHBd,minwHBd,minHBa,minwHBa,minHBint2,minHBint3,minHBint4,minHBint5,minHBint6,minHBint7,minHBint8,minHBint9,minHBint10,minHsOH,minHdNH,minHsSH,minHsNH2,minHssNH,minHaaNH,minHsNH3p,minHssNH2p,minHsssNHp,minHtCH,minHdCH2,minHdsCH,minHaaCH,minHCHnX,minHCsats,minHCsatu,minHAvin,minHother,minHmisc,minsLi,minssBe,minssssBem,minsBH2,minssBH,minsssB,minssssBm,minsCH3,mindCH2,minssCH2,mintCH,mindsCH,minaaCH,minsssCH,minddC,mintsC,mindssC,minaasC,minaaaC,minssssC,minsNH3p,minsNH2,minssNH2p,mindNH,minssNH,minaaNH,mintN,minsssNHp,mindsN,minaaN,minsssN,minddsN,minaasN,minssssNp,minsOH,mindO,minssO,minaaO,minaOm,minsOm,minsF,minsSiH3,minssSiH2,minsssSiH,minssssSi,minsPH2,minssPH,minsssP,mindsssP,minddsP,minsssssP,minsSH,mindS,minssS,minaaS,mindssS,minddssS,minssssssS,minSm,minsCl,minsGeH3,minssGeH2,minsssGeH,minssssGe,minsAsH2,minssAsH,minsssAs,mindsssAs,minddsAs,minsssssAs,minsSeH,mindSe,minssSe,minaaSe,mindssSe,minssssssSe,minddssSe,minsBr,minsSnH3,minssSnH2,minsssSnH,minssssSn,minsI,minsPbH3,minssPbH2,minsssPbH,minssssPb,maxHBd,maxwHBd,maxHBa,maxwHBa,maxHBint2,maxHBint3,maxHBint4,maxHBint5,maxHBint6,maxHBint7,maxHBint8,maxHBint9,maxHBint10,maxHsOH,maxHdNH,maxHsSH,maxHsNH2,maxHssNH,maxHaaNH,maxHsNH3p,maxHssNH2p,maxHsssNHp,maxHtCH,maxHdCH2,maxHdsCH,maxHaaCH,maxHCHnX,maxHCsats,maxHCsatu,maxHAvin,maxHother,maxHmisc,maxsLi,maxssBe,maxssssBem,maxsBH2,maxssBH,maxsssB,maxssssBm,maxsCH3,maxdCH2,maxssCH2,maxtCH,maxdsCH,maxaaCH,maxsssCH,maxddC,maxtsC,maxdssC,maxaasC,maxaaaC,maxssssC,maxsNH3p,maxsNH2,maxssNH2p,maxdNH,maxssNH,maxaaNH,maxtN,maxsssNHp,maxdsN,maxaaN,maxsssN,maxddsN,maxaasN,maxssssNp,maxsOH,maxdO,maxssO,maxaaO,maxaOm,maxsOm,maxsF,maxsSiH3,maxssSiH2,maxsssSiH,maxssssSi,maxsPH2,maxssPH,maxsssP,maxdsssP,maxddsP,maxsssssP,maxsSH,maxdS,maxssS,maxaaS,maxdssS,maxddssS,maxssssssS,maxSm,maxsCl,maxsGeH3,maxssGeH2,maxsssGeH,maxssssGe,maxsAsH2,maxssAsH,maxsssAs,maxdsssAs,maxddsAs,maxsssssAs,maxsSeH,maxdSe,maxssSe,maxaaSe,maxdssSe,maxssssssSe,maxddssSe,maxsBr,maxsSnH3,maxssSnH2,maxsssSnH,maxssssSn,maxsI,maxsPbH3,maxssPbH2,maxsssPbH,maxssssPb,sumI,meanI,hmax,gmax,hmin,gmin,LipoaffinityIndex,MAXDN,MAXDP,DELS,MAXDN2,MAXDP2,DELS2,ETA_Alpha,ETA_AlphaP,ETA_dAlpha_A,ETA_dAlpha_B,ETA_Epsilon_1,ETA_Epsilon_2,ETA_Epsilon_3,ETA_Epsilon_4,ETA_Epsilon_5,ETA_dEpsilon_A,ETA_dEpsilon_B,ETA_dEpsilon_C,ETA_dEpsilon_D,ETA_Psi_1,ETA_dPsi_A,ETA_dPsi_B,ETA_Shape_P,ETA_Shape_Y,ETA_Shape_X,ETA_Beta,ETA_BetaP,ETA_Beta_s,ETA_BetaP_s,ETA_Beta_ns,ETA_BetaP_ns,ETA_dBeta,ETA_dBetaP,ETA_Beta_ns_d,ETA_BetaP_ns_d,ETA_Eta,ETA_EtaP,ETA_Eta_R,ETA_Eta_F,ETA_EtaP_F,ETA_Eta_L,ETA_EtaP_L,ETA_Eta_R_L,ETA_Eta_F_L,ETA_EtaP_F_L,ETA_Eta_B,ETA_EtaP_B,ETA_Eta_B_RC,ETA_EtaP_B_RC,FMF,fragC,nHBAcc,nHBAcc2,nHBAcc3,nHBAcc_Lipinski,nHBDon,nHBDon_Lipinski,HybRatio,IC0,IC1,IC2,IC3,IC4,IC5,TIC0,TIC1,TIC2,TIC3,TIC4,TIC5,SIC0,SIC1,SIC2,SIC3,SIC4,SIC5,CIC0,CIC1,CIC2,CIC3,CIC4,CIC5,BIC0,BIC1,BIC2,BIC3,BIC4,BIC5,MIC0,MIC1,MIC2,MIC3,MIC4,MIC5,ZMIC0,ZMIC1,ZMIC2,ZMIC3,ZMIC4,ZMIC5,Kier1,Kier2,Kier3,nAtomLC,nAtomP,nAtomLAC,MLogP,McGowan_Volume,MDEC-11,MDEC-12,MDEC-13,MDEC-14,MDEC-22,MDEC-23,MDEC-24,MDEC-33,MDEC-34,MDEC-44,MDEO-11,MDEO-12,MDEO-22,MDEN-11,MDEN-12,MDEN-13,MDEN-22,MDEN-23,MDEN-33,MLFER_A,MLFER_BH,MLFER_BO,MLFER_S,MLFER_E,MLFER_L,MPC2,MPC3,MPC4,MPC5,MPC6,MPC7,MPC8,MPC9,MPC10,TPC,piPC1,piPC2,piPC3,piPC4,piPC5,piPC6,piPC7,piPC8,piPC9,piPC10,TpiPC,R_TpiPCTPC,PetitjeanNumber,nRing,n3Ring,n4Ring,n5Ring,n6Ring,n7Ring,n8Ring,n9Ring,n10Ring,n11Ring,n12Ring,nG12Ring,nFRing,nF4Ring,nF5Ring,nF6Ring,nF7Ring,nF8Ring,nF9Ring,nF10Ring,nF11Ring,nF12Ring,nFG12Ring,nTRing,nT4Ring,nT5Ring,nT6Ring,nT7Ring,nT8Ring,nT9Ring,nT10Ring,nT11Ring,nT12Ring,nTG12Ring,nHeteroRing,n3HeteroRing,n4HeteroRing,n5HeteroRing,n6HeteroRing,n7HeteroRing,n8HeteroRing,n9HeteroRing,n10HeteroRing,n11HeteroRing,n12HeteroRing,nG12HeteroRing,nFHeteroRing,nF4HeteroRing,nF5HeteroRing,nF6HeteroRing,nF7HeteroRing,nF8HeteroRing,nF9HeteroRing,nF10HeteroRing,nF11HeteroRing,nF12HeteroRing,nFG12HeteroRing,nTHeteroRing,nT4HeteroRing,nT5HeteroRing,nT6HeteroRing,nT7HeteroRing,nT8HeteroRing,nT9HeteroRing,nT10HeteroRing,nT11HeteroRing,nT12HeteroRing,nTG12HeteroRing,nRotB,RotBFrac,nRotBt,RotBtFrac,LipinskiFailures,topoRadius,topoDiameter,topoShape,GGI1,GGI2,GGI3,GGI4,GGI5,GGI6,GGI7,GGI8,GGI9,GGI10,JGI1,JGI2,JGI3,JGI4,JGI5,JGI6,JGI7,JGI8,JGI9,JGI10,JGT,SpMax_D,SpDiam_D,SpAD_D,SpMAD_D,EE_D,VE1_D,VE2_D,VE3_D,VR1_D,VR2_D,VR3_D,TopoPSA,VABC,VAdjMat,MWC2,MWC3,MWC4,MWC5,MWC6,MWC7,MWC8,MWC9,MWC10,TWC,SRW2,SRW3,SRW4,SRW5,SRW6,SRW7,SRW8,SRW9,SRW10,TSRW,MW,AMW,WTPT-1,WTPT-2,WTPT-3,WTPT-4,WTPT-5,WPATH,WPOL,XLogP,Zagreb,TDB1u,TDB2u,TDB3u,TDB4u,TDB5u,TDB6u,TDB7u,TDB8u,TDB9u,TDB10u,TDB1m,TDB2m,TDB3m,TDB4m,TDB5m,TDB6m,TDB7m,TDB8m,TDB9m,TDB10m,TDB1v,TDB2v,TDB3v,TDB4v,TDB5v,TDB6v,TDB7v,TDB8v,TDB9v,TDB10v,TDB1e,TDB2e,TDB3e,TDB4e,TDB5e,TDB6e,TDB7e,TDB8e,TDB9e,TDB10e,TDB1p,TDB2p,TDB3p,TDB4p,TDB5p,TDB6p,TDB7p,TDB8p,TDB9p,TDB10p,TDB1i,TDB2i,TDB3i,TDB4i,TDB5i,TDB6i,TDB7i,TDB8i,TDB9i,TDB10i,TDB1s,TDB2s,TDB3s,TDB4s,TDB5s,TDB6s,TDB7s,TDB8s,TDB9s,TDB10s,TDB1r,TDB2r,TDB3r,TDB4r,TDB5r,TDB6r,TDB7r,TDB8r,TDB9r,TDB10r,PPSA-1,PPSA-2,PPSA-3,PNSA-1,PNSA-2,PNSA-3,DPSA-1,DPSA-2,DPSA-3,FPSA-1,FPSA-2,FPSA-3,FNSA-1,FNSA-2,FNSA-3,WPSA-1,WPSA-2,WPSA-3,WNSA-1,WNSA-2,WNSA-3,RPCG,RNCG,RPCS,RNCS,THSA,TPSA,RHSA,RPSA,GRAV-1,GRAV-2,GRAV-3,GRAVH-1,GRAVH-2,GRAVH-3,GRAV-4,GRAV-5,GRAV-6,LOBMAX,LOBMIN,MOMI-X,MOMI-Y,MOMI-Z,MOMI-XY,MOMI-XZ,MOMI-YZ,MOMI-R,geomRadius,geomDiameter,geomShape,RDF10u,RDF15u,RDF20u,RDF25u,RDF30u,RDF35u,RDF40u,RDF45u,RDF50u,RDF55u,RDF60u,RDF65u,RDF70u,RDF75u,RDF80u,RDF85u,RDF90u,RDF95u,RDF100u,RDF105u,RDF110u,RDF115u,RDF120u,RDF125u,RDF130u,RDF135u,RDF140u,RDF145u,RDF150u,RDF155u,RDF10m,RDF15m,RDF20m,RDF25m,RDF30m,RDF35m,RDF40m,RDF45m,RDF50m,RDF55m,RDF60m,RDF65m,RDF70m,RDF75m,RDF80m,RDF85m,RDF90m,RDF95m,RDF100m,RDF105m,RDF110m,RDF115m,RDF120m,RDF125m,RDF130m,RDF135m,RDF140m,RDF145m,RDF150m,RDF155m,RDF10v,RDF15v,RDF20v,RDF25v,RDF30v,RDF35v,RDF40v,RDF45v,RDF50v,RDF55v,RDF60v,RDF65v,RDF70v,RDF75v,RDF80v,RDF85v,RDF90v,RDF95v,RDF100v,RDF105v,RDF110v,RDF115v,RDF120v,RDF125v,RDF130v,RDF135v,RDF140v,RDF145v,RDF150v,RDF155v,RDF10e,RDF15e,RDF20e,RDF25e,RDF30e,RDF35e,RDF40e,RDF45e,RDF50e,RDF55e,RDF60e,RDF65e,RDF70e,RDF75e,RDF80e,RDF85e,RDF90e,RDF95e,RDF100e,RDF105e,RDF110e,RDF115e,RDF120e,RDF125e,RDF130e,RDF135e,RDF140e,RDF145e,RDF150e,RDF155e,RDF10p,RDF15p,RDF20p,RDF25p,RDF30p,RDF35p,RDF40p,RDF45p,RDF50p,RDF55p,RDF60p,RDF65p,RDF70p,RDF75p,RDF80p,RDF85p,RDF90p,RDF95p,RDF100p,RDF105p,RDF110p,RDF115p,RDF120p,RDF125p,RDF130p,RDF135p,RDF140p,RDF145p,RDF150p,RDF155p,RDF10i,RDF15i,RDF20i,RDF25i,RDF30i,RDF35i,RDF40i,RDF45i,RDF50i,RDF55i,RDF60i,RDF65i,RDF70i,RDF75i,RDF80i,RDF85i,RDF90i,RDF95i,RDF100i,RDF105i,RDF110i,RDF115i,RDF120i,RDF125i,RDF130i,RDF135i,RDF140i,RDF145i,RDF150i,RDF155i,RDF10s,RDF15s,RDF20s,RDF25s,RDF30s,RDF35s,RDF40s,RDF45s,RDF50s,RDF55s,RDF60s,RDF65s,RDF70s,RDF75s,RDF80s,RDF85s,RDF90s,RDF95s,RDF100s,RDF105s,RDF110s,RDF115s,RDF120s,RDF125s,RDF130s,RDF135s,RDF140s,RDF145s,RDF150s,RDF155s,L1u,L2u,L3u,P1u,P2u,E1u,E2u,E3u,Tu,Au,Vu,Ku,Du,L1m,L2m,L3m,P1m,P2m,E1m,E2m,E3m,Tm,Am,Vm,Km,Dm,L1v,L2v,L3v,P1v,P2v,E1v,E2v,E3v,Tv,Av,Vv,Kv,Dv,L1e,L2e,L3e,P1e,P2e,E1e,E2e,E3e,Te,Ae,Ve,Ke,De,L1p,L2p,L3p,P1p,P2p,E1p,E2p,E3p,Tp,Ap,Vp,Kp,Dp,L1i,L2i,L3i,P1i,P2i,E1i,E2i,E3i,Ti,Ai,Vi,Ki,Di,L1s,L2s,L3s,P1s,P2s,E1s,E2s,E3s,Ts,As,Vs,Ks,Ds
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+UMLYSI0118,L,methylcyclopentadiene dimer,CC1=CC2C3CC(C=C3C)C2C1,aromatic,C12H16,26472-00-4,n/a,UML,"2,5-Dimethyl-3a,4,7,7a-tetrahydro-1H-4,7-methanoindene",189.2,0,1.5312,2.34457344,51.5726,31.788688,0,0,28,12,16,0,12,0,0,0,0,0,0,0,0,0,1747.426476,2213.411102,3545.151814,2841.028359,1841.420961,1030.265549,399.614713,87.883488,9.144576,5579.542732,7765.022486,13008.14029,13005.18862,9687.437314,5689.039018,3122.337907,1154.675865,279.7536999,197.981616,219.449336,433.168024,578.480604,595.232964,402.962036,237.233188,143.482752,60.466176,40.58060648,56.86130896,98.31903254,107.969179,86.98579978,52.89440628,30.83975396,13.35045943,4.001516144,4480.214705,4225.081749,8862.493482,12357.74964,13859.49638,9703.319939,5593.174787,3692.500067,1664.258868,49.16666667,62.05555556,108.5555556,139.75,128.1111111,85,52,27,9,62.40808843,73.78037007,59.08586357,35.07442419,21.66377601,17.76319912,11.75337391,4.184928,1.016064,199.2693833,258.8340829,216.8023381,160.5578841,113.9698508,98.08687962,91.83346785,54.98456499,31.08374443,7.070772,7.314977867,7.219467067,7.141735852,7.002740753,6.94762131,6.977446706,6.832512,6.718464,1.449307374,1.895376965,1.638650542,1.332952827,1.02336235,0.911972522,0.907051587,0.635736163,0.444612905,160.0076681,140.8360583,147.7082247,152.5648103,163.0528986,167.2986196,164.5051408,175.8333365,184.917652,1.755952381,2.068518519,1.809259259,1.725308642,1.507189542,1.465517241,1.529411765,1.285714286,1,0.050905529,0.004217743,-0.00827758,-0.043317236,0.017157192,0.004599518,-0.013864359,0.007255213,0.006776745,830.1669189,79.06351608,158.1270322,-793.1058957,-133.4196834,182.8343809,-264.3686319,155.6562973,200.1295251,1543.730835,147.0219843,294.0439686,-1474.81428,-248.0995985,339.9883387,-491.60476,289.4495316,372.1493977,0.162624,0.015488,0.030976,-0.155364,-0.026136,0.035816,-0.051788,0.030492,0.039204,6.901195096,0.657256676,1.314513352,-6.593106029,-1.10912064,1.519906063,-2.19770201,1.293974081,1.663680961,37.48740129,3.570228694,7.140457389,-35.81385659,-6.024760922,8.256153856,-11.9379522,7.028887742,9.037141383,3.734126984,-0.512755102,0.615362812,-3.326105442,0.654336735,1.201530612,-1.105725624,-0.068452381,0.674744898,0.001818055,1.41E-04,-1.38E-04,-5.35E-04,2.02E-04,7.93E-05,-4.08E-04,3.45E-04,7.53E-04,29.64881853,2.635450536,2.635450536,-9.791430811,-1.569643334,3.152316913,-7.775547997,7.412204633,22.2366139,55.13324411,4.90073281,4.90073281,-18.2075837,-2.918818806,5.861867908,-14.45896353,13.78331103,41.34993308,0.005808,5.16E-04,5.16E-04,-0.001918074,-3.07E-04,6.18E-04,-0.001523176,0.001452,0.004356,0.246471253,0.021908556,0.021908556,-0.081396371,-0.013048478,0.026205277,-0.064638294,0.061617813,0.18485344,1.33883576,0.119007623,0.119007623,-0.442146378,-0.07087954,0.14234748,-0.351116241,0.33470894,1.00412682,0.133361678,-0.017091837,0.010256047,-0.04106303,0.007698079,0.020716045,-0.032521342,-0.003259637,0.074971655,0.07733069,-0.075883124,-0.294149962,0.111024893,0.043619174,-0.224292093,0.190030769,0.414163409,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,0.088888889,0.088888889,-0.330246914,-0.052941176,0.106321839,-0.262254902,0.25,0.75,-0.12816153,0.076904003,-0.30790727,0.057723323,0.155337315,-0.243858223,-0.024442083,0.562167906,1.000096072,1.120584679,1.344555968,0.840885103,0.870775307,1.208667786,0.418004626,2.10E-08,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.05,0.984375,1.336805556,0.972794118,0.780711207,1.158088235,0.5625,0,1.325611052,0.76825186,1.345200142,0.831755954,0.806863571,1.293711321,1.032943677,0,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,54.31629427,27.12088121,39.73276862,54.31629427,4.526357856,27.12088121,0,0.061266924,0.005105577,-3.351018189,82.92714841,6.910595701,5.301554991,64.0130685,31.57235902,47.47158148,63.78220431,5.315183692,31.57235902,-0.346296296,0.04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+UMLYSI0127,L,"1,2,3,4,4a,5,6,7,8,8a-decahydronaphthalene",C1CCC2CCCCC2C1,aromatic,C10H18,91-17-8,7044,UML,"stereochemistry not specifiied (see below)


+


+


+


+cis: C1C[C@H]2CCCC[C@H]2CC1


+


+


+


+trans: 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+UMLYSI0172,L,ethylcyclopentane,CCC1CCCC1,cyclic 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+UMLYSI0223,L,3-methylbutyl acetate,CC(C)CCOC(=O)C,saturated 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+UMLYSI0228,L,butyl propanoate,CCCCOC(=O)CC,saturated 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+UMLYSI0239,L,4-methylpentan-2-ol,CC(C)CC(C)O,saturated 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+UMLYSI0240,L,methyl (E)-2-methylpent-2-enoate,CC/C=C(\C)/C(=O)OC,unsaturated 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+UMLYSI0242,L,2-methylhexan-2-ol,CCCCC(C)(C)O,saturated 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+UMLYSI0245,L,ethyl pent-4-enoate,CCOC(=O)CCC=C,unsaturated 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+UMLYSI0246,L,2-methylpropyl propanoate,CCC(=O)OCC(C)C,saturated 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+UMLYSI0251,L,tert-butyl propanoate,CCC(=O)OC(C)(C)C,saturated 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+UMLYSI0255,L,ethyl 3-methylbut-2-enoate,CCOC(=O)C=C(C)C,unsaturated 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+UMLYSI0258,L,methyl hex-5-enoate,COC(=O)CCCC=C,unsaturated 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+UMLYSI0259,L,methyl octanoate,CCCCCCCC(=O)OC,saturated 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+UMLYSI0260,L,methyl (E)-pent-3-enoate,C/C=C/CC(=O)OC,unsaturated 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+UMLYSI0266,L,3-methylbutyl formate,CC(C)CCOC=O,saturated 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+UMLYSI0269,L,methyl (E)-2-methylbut-2-enoate,C/C=C(\C)/C(=O)OC,unsaturated 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+UMLYSI0272,L,3-methylpentan-2-ol,CCC(C)C(C)O,saturated 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+UMLYSI0274,L,propyl butanoate,CCCC(=O)OCCC,saturated 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+UMLYSI0275,L,propyl 2-methylprop-2-enoate,CCCOC(=O)C(=C)C,unsaturated 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+UMLYSI0277,L,ethyl 3-methylbutanoate,CCOC(=O)CC(C)C,saturated 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+UMLYSI0279,L,methyl 3-methylbut-2-enoate,CC(=CC(=O)OC)C,unsaturated 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+UMLYSI0283,L,butyl acetate,CCCCOC(=O)C,saturated 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+UMLYSI0286,L,methyl (E)-hex-2-enoate,CCC/C=C/C(=O)OC,unsaturated 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+UMLYSI0288,L,2-methylpentan-1-ol,CCCC(C)CO,saturated 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+UMLYSI0291,L,butan-2-yl acetate,CCC(C)OC(=O)C,saturated 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+UMLYSI0292,L,2-methylpropyl acetate,CC(C)COC(=O)C,saturated 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+UMLYSI0294,L,dipropyl carbonate,CCCOC(=O)OCCC,other multi-oxygen 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+UMLYSI0297,L,ethyl 2-methylbut-2-enoate,CCOC(=O)C(=CC)C,unsaturated 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+UMLYSI0300,L,methyl (E)-pent-2-enoate,CC/C=C/C(=O)OC,unsaturated 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+UMLYSI0302,L,methyl pent-4-enoate,COC(=O)CCC=C,unsaturated 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+UMLYSI0304,L,2-methylbutan-2-ol,CCC(C)(C)O,saturated 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+UMLYSI0306,L,tert-butyl acetate,CC(=O)OC(C)(C)C,saturated 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+UMLYSI0307,L,methyl heptanoate,CCCCCCC(=O)OC,saturated 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+UMLYSI0313,L,2-methoxy-2-methylpropane,CC(C)(C)OC,saturated 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+UMLYSI0315,L,"methyl 2,2-dimethylpropanoate",CC(C)(C)C(=O)OC,saturated 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+UMLYSI0321,L,ethyl (E)-but-2-enoate,CCOC(=O)/C=C/C,unsaturated 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+UMLYSI0323,L,tert-butyl formate,CC(C)(C)OC=O,saturated 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+UMLYSI0325,L,"2,2-dimethylbutanoic acid",CCC(C)(C)C(=O)O,saturated 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+UMLYSI0327,L,ethyl 2-methylbutanoate,CCC(C)C(=O)OCC,saturated 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+UMLYSI0329,L,methyl 2-methylprop-2-enoate,CC(=C)C(=O)OC,unsaturated 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+UMLYSI0331,L,3-methylpentan-2-one,CCC(C)C(=O)C,saturated 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+UMLYSI0334,L,methyl 3-methylbutanoate,CC(C)CC(=O)OC,saturated 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+UMLYSI0335,L,ethyl pentanoate,CCCCC(=O)OCC,saturated 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+UMLYSI0339,L,2-methylpropan-2-ol,CC(C)(C)O,saturated 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+UMLYSI0343,L,methyl hexanoate,CCCCCC(=O)OC,saturated 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+UMLYSI0344,L,3-methylbutan-2-one,CC(C)C(=O)C,saturated 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+UMLYSI0347,L,ethyl 2-methylpropanoate,CCOC(=O)C(C)C,saturated 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+UMLYSI0348,L,2-ethylbutanoic acid,CCC(CC)C(=O)O,saturated 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+UMLYSI0353,L,ethyl butanoate,CCCC(=O)OCC,saturated 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+UMLYSI0355,L,ethyl 2-methylprop-2-enoate,CCOC(=O)C(=C)C,unsaturated 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+UMLYSI0357,L,2-methylbut-3-en-2-ol,CC(C)(C=C)O,unsaturated 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+UMLYSI0358,L,methyl 2-methylbutanoate,CCC(C)C(=O)OC,saturated 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+UMLYSI0361,L,propyl acetate,CCCOC(=O)C,saturated 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+UMLYSI0362,L,propan-2-yl acetate,CC(C)OC(=O)C,saturated 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+UMLYSI0366,L,methyl pentanoate,CCCCC(=O)OC,saturated 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+UMLYSI0367,L,methyl 2-methylpropanoate,CC(C)C(=O)OC,saturated 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+UMLYSI0370,L,ethyl prop-2-enoate,CCOC(=O)C=C,unsaturated 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+UMLYSI0380,L,ethyl propanoate,CCC(=O)OCC,saturated 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+UMLYSI0382,L,methyl prop-2-enoate,COC(=O)C=C,unsaturated 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+UMLYSI0387,L,"1,2-dimethoxypropane",CC(COC)OC,other multi-oxygen 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+UMLYSI0388,L,ethoxymethoxyethane,CCOCOCC,other multi-oxygen 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+UMLYSI0389,L,butanoic acid,CCCC(=O)O,saturated 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+UMLYSI0400,L,diethyl carbonate,CCOC(=O)OCC,other multi-oxygen compounds,C5H10O3,105-58-8,7766,PubChem,,15.3,0,1.1651,1.35745801,27.624,17.87393,0,0,18,8,10,0,5,0,3,0,0,0,0,0,0,0,1499.385248,1370.419067,1634.467583,1214.56727,1114.578541,530.480498,269.399257,84.835296,9.144576,3077.594284,3508.047478,3825.75578,3381.984994,2978.963653,2290.143836,1695.218043,1061.424631,279.7536999,144.942368,136.426772,238.012652,221.135672,166.151188,157.205576,105.590692,123.32736,60.466176,20.32024105,23.4099431,27.01751028,23.93330001,19.10566975,16.44919307,15.70279472,12.01662072,4.001516144,3039.502158,2551.530831,4867.933027,4809.569701,3140.37717,3088.290223,2397.690441,3137.747111,1664.258868,98.77777778,57.83333333,119.25,77.83333333,96.25,75,25,24,9,83.29918044,80.61288629,56.36095114,43.3774025,58.66202847,29.47113878,17.95995047,4.713072,1.016064,170.9774602,206.355734,131.9226131,120.7851784,156.7875607,127.2302131,113.0145362,58.96803508,31.08374443,8.052353778,8.025104235,8.207332828,7.897702571,8.744799368,8.733643111,7.039379467,6.85152,6.718464,1.12890228,1.377055476,0.931638286,0.854760715,1.005561566,0.91384406,1.046852981,0.66759004,0.444612905,168.861231,150.0900489,167.8597595,171.7703465,165.2830089,171.5716791,159.8460294,174.3192839,184.917652,5.487654321,3.401960784,4.112068966,2.779761905,5.065789474,4.166666667,1.666666667,1.333333333,1,0.415118885,-0.361660448,0.232854771,-0.026989395,-0.011207783,-0.071160166,0.013813294,0.011029841,0.005760444,724.0980578,13.77221954,163.8539824,199.7273432,-365.5964595,-690.6893633,-149.5274418,188.6995756,277.7111151,793.1399219,-196.0811423,-76.09792379,360.7851222,-259.7236183,-273.7112397,-313.7241923,70.56357046,291.4194629,2.632671111,-0.12377916,1.816445432,-0.686101728,-1.611346173,-2.154741333,0.342100741,0.666366222,0.434720444,3.453944568,-1.711488211,-0.353453644,1.062387277,-0.841996373,0.73981844,-1.258754837,-0.179994762,0.816509827,19.81899686,-10.84089604,-1.069533101,4.438474164,-4.761457742,6.515020061,-6.400508425,-1.548957287,3.758359945,39.49382716,-2.32441701,21.78772291,-4.169410151,-19.02434842,-32.67901235,3.625514403,7.061728395,5.975308642,0.02306216,-0.021274144,0.008029475,-9.64E-04,-5.90E-04,-0.003953343,9.21E-04,6.13E-04,6.40E-04,40.22766988,0.810130561,5.650137323,7.133119401,-19.24191892,-38.3716313,-9.968496121,10.48330975,30.85679057,44.063329,-11.53418484,-2.624066338,12.88518294,-13.66966412,-15.20617999,-20.91494615,3.920198359,32.37994032,0.146259506,-0.007281127,0.062636049,-0.024503633,-0.084807693,-0.119707852,0.022806716,0.037020346,0.048302272,0.191885809,-0.100675777,-0.012188057,0.037942403,-0.044315599,0.041101024,-0.083916989,-0.009999709,0.090723314,1.101055381,-0.637699767,-0.036880452,0.158516934,-0.250603039,0.361945559,-0.426700562,-0.086053183,0.417595549,2.194101509,-0.136730412,0.75130079,-0.148907505,-1.001281496,-1.815500686,0.24170096,0.392318244,0.663923182,-0.922469696,0.348166639,-0.041796039,-0.025577974,-0.171421173,0.039930616,0.026570318,0.027753225,0.02013864,0.140454004,0.177318732,-0.478325466,-0.953861643,-0.247801977,0.260599478,0.767053888,-0.261763809,-0.059552158,0.292424182,-0.310227675,-0.345098301,-0.474656514,0.088967367,0.734850068,-0.049782249,0.428252843,-0.16753532,-0.579843974,-0.818462027,0.155933222,0.253114116,0.330250476,-0.524665047,-0.063517238,0.197734282,-0.230947764,0.214195227,-0.437327749,-0.052112812,0.472798455,-0.579171383,-0.033495547,0.143968176,-0.227602574,0.328726025,-0.387537783,-0.078155181,0.379268433,-0.062317268,0.342418428,-0.067867191,-0.456351491,-0.827446077,0.110159425,0.178805877,0.302594561,2.008863862,0.643239569,1.397726163,2.029972582,1.156134425,0.059871496,0.043392396,0,0.890888186,0.866800299,0.794989966,1.598849524,2.074512812,0.947441732,0.473720866,0,1.527799472,0.980766394,0.589042042,1.216163372,1.13234557,1.608445085,0.804222543,0,0.827957579,0.712249529,1.171517449,2.11781474,2.444632321,0.051047268,0.025523634,0,2.002256055,0.988133822,0.628341307,1.191424325,0.580384416,1.651176165,0.825588083,0,2.080429519,0.972949128,0.67557861,1.242412929,0.521144898,1.563104941,0.78155247,0,0.641450453,0.441991571,0.830147814,2.561914908,3.048999687,0.078915286,0.071741169,0,29.57698318,15.16349159,22.99376125,30.13948318,3.767435397,15.16349261,0.75,3.33E-16,4.16E-17,-28.51055062,3000011.092,375001.3865,11.93130124,29.5999863,15.17389152,23.01077582,30.16083385,3.770104232,15.17389253,0.747796737,2.22E-16,2.78E-17,-28.83492271,3000011.09,375001.3863,11.93130123,50.00638931,24.40470294,38.15826509,49.10865174,6.138581467,24.40470294,-1.196983434,2.22E-16,2.78E-17,-28.83492271,3000010.425,375001.3031,11.93130106,29.62412864,15.18480652,23.02863331,30.18324194,3.772905243,15.18480752,0.745484401,7.77E-16,9.71E-17,-27.83271234,3000011.088,375001.386,11.93130123,71.61928722,34.18620221,54.23974261,69.18412513,8.648015641,34.18620221,-3.246882793,3.33E-16,4.16E-17,-28.51055062,3000010.219,375001.2774,11.931301,31.98659218,16.25299737,24.77735118,32.3761441,4.047018012,16.25299769,0.519402558,3.33E-16,4.16E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+UMLYSI0401,L,methyl propanoate,CCC(=O)OC,saturated 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+UMLYSI0408,L,ethyl acetate,CCOC(=O)C,saturated 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+UMLYSI0413,L,methyl acetate,CC(=O)OC,saturated 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+UMLYSI0415,L,dimethyl carbonate,COC(=O)OC,other multi-oxygen 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+UMLYSI0441,L,ethyl (E)-2-methylbut-2-enoate,CCOC(=O)/C(=C/C)/C,unsaturated 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+UMLYSI0455,L,2-methylpropan-2-amine,CC(C)(C)N,saturated 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+UMLYSI0456,L,N-ethylethanamine,CCNCC,saturated 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+UMLYSI0460,L,"N,N-dimethylbutan-1-amine",CCCCN(C)C,saturated 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816735881,7.598399329,8.386400901,9.169830829,9.957265258,79.38866433,2.944438979,0,4.143134726,0,5.556828062,0,7.055312843,0,8.590257762,8.836955157,120.0575149,7.062206757,17.52861439,1.947623821,2.407582445,2.407582445,0,88,9,1.244,40,1.235311985,2.180526754,3.016293031,3.877364212,4.545722017,5.507008027,6.26362303,0,0,0,112.862153,158.3064207,162.0263884,164.8896225,111.9051583,35.43795498,6.364241871,0,0,0,353.9540914,506.7350021,563.2047089,596.233247,494.2999454,362.8108035,194.6968575,0,0,0,9.272066401,16.26974321,22.87481008,29.06995299,33.65435502,39.24472494,42.08192584,0,0,0,2.470578406,3.640152951,4.199234316,4.64092369,4.215445187,3.812616483,2.78488763,0,0,0,172.2139809,319.8118962,462.5551441,609.3293798,753.4052087,955.9195456,1158.254464,0,0,0,4.183037302,7.163553104,10.45881942,12.9640468,13.11432842,10.65845041,6.26362303,0,0,0,0.521224756,0.788554686,0.943181725,1.069875409,0.996233519,0.889015444,0.641394998,0,0,0,232.024878,135.2231437,14.38891238,116.5850842,-67.94530717,-16.64005361,115.4397937,203.1684509,31.02896599,0.665571565,0.387892368,0.041275104,0.334428435,-0.194903515,-0.047732582,80.88618394,47.14013503,5.016118201,40.64272181,-23.68641096,-5.800888459,0.273667598,0.504466152,7.203621162,22.3400992,304.3253277,44.28463454,0.87296796,0.12703204,750.0902004,27.38777465,9.085967183,828.6480505,28.78624759,9.392691053,1268.927046,35.62200227,10.82627077,1.908523254,1.908523254,462.8541358,397.4045083,133.9027107,1.164692715,3.456645003,2.967860069,3.902440363,3.785524729,7.010507493,0.851924897,2.652835219,1.974468238,3.161380502,6.88927335,2.880493086,5.985429103,3.715528733,3.843300967,3.468805895,1.269385255,3.06567531,0.005896557,0.989019984,3.93E-11,3.01E-43,4.45E-97,1.27E-172,6.96E-270,0,0,0,0,0,0,0,0,0,0,0,0,0.239241537,1.974472691,0.256574426,1.88880276,1.139584144,2.196833846,1.461327573,1.708604837,0.533988256,0.107807191,0.142697436,4.15E-05,0.006965749,2.77E-13,2.12E-45,3.13E-99,8.92E-175,4.90E-272,0,0,0,0,0,0,0,0,0,0,0,0,0.724646387,1.97435467,0.826268767,2.222189809,1.316450541,2.71885313,1.724301589,1.823880094,0.963728168,0.340212713,0.525782914,4.33E-04,0.072588471,2.88E-12,2.21E-44,3.26E-98,9.30E-174,5.11E-271,0,0,0,0,0,0,0,0,0,0,0,0,2.509177848,1.974527098,2.97660226,6.822189165,3.114507867,5.996812632,3.643851072,3.824662877,3.278142857,1.212773481,2.835245439,0.005253727,0.881199,3.50E-11,2.68E-43,3.96E-97,1.13E-172,6.20E-270,0,0,0,0,0,0,0,0,0,0,0,0,1.060361215,1.974313579,1.224311651,2.57552177,1.412234579,3.068644013,1.970375647,2.009778538,1.297069457,0.497049087,0.846067766,9.40E-04,0.157671859,6.26E-12,4.80E-44,7.09E-98,2.02E-173,1.11E-270,0,0,0,0,0,0,0,0,0,0,0,0,3.203679959,1.974537718,3.8579247,9.105586268,3.563414592,7.354474124,4.387128623,4.582771071,4.434053935,1.544877889,4.09992019,0.008599575,1.442392698,5.73E-11,4.39E-43,6.48E-97,1.85E-172,1.02E-269,0,0,0,0,0,0,0,0,0,0,0,0,1.479098251,1.872983686,1.604355125,5.582219875,3.7589822,5.003856907,3.137857712,3.797306818,1.952210849,0.754636254,1.351210685,0.001605112,0.269222831,1.07E-11,8.19E-44,1.21E-97,3.45E-173,1.90E-270,0,0,0,0,0,0,0,0,0,0,0,0,4.11421028,1.546031486,0.360663114,0.683320923,0.256777265,0.552614424,0.440013325,0.213134539,6.020904881,8.402139057,16.71711332,0.524981384,1.205762288,3.030483648,0.862538224,0.327650482,0.71800969,0.204360384,0.319634046,0.148976046,0.148106006,4.220672354,3.889458478,8.966579043,0.577014535,0.616716099,3.286165315,1.072310157,0.295306432,0.706127916,0.230416934,0.355115416,0.213973511,0.145709583,4.653781904,4.810874285,10.50525358,0.559191873,0.714798511,4.065377885,1.511453691,0.396990188,0.680532169,0.253012854,0.540522538,0.422957692,0.263760879,5.973821765,8.35857783,16.77175758,0.520798254,1.22724111,3.482308392,1.196158132,0.268284297,0.703958723,0.241806829,0.396157738,0.263250391,0.117979507,4.946750822,5.420550607,11.48481056,0.555938085,0.777387635,4.248740619,1.619938651,0.383489629,0.679562675,0.25910027,0.589333568,0.483263597,0.241356125,6.252168899,9.133276782,18.02488942,0.519344012,1.313953289,4.06599169,1.511400532,0.396857989,0.680586106,0.252985811,0.540681155,0.422937396,0.263610886,5.974250211,8.358774664,16.77185294,0.52087916,1.227229438
diff --git a/ecnet/__init__.py b/ecnet/__init__.py
index af01be1..b6bce23 100644
--- a/ecnet/__init__.py
+++ b/ecnet/__init__.py
@@ -1,2 +1,2 @@
 from ecnet.server import Server
-__version__ = '3.2.3'
+__version__ = '3.3.0'
diff --git a/ecnet/models/mlp.py b/ecnet/models/mlp.py
index 2ef2eab..2755e99 100644
--- a/ecnet/models/mlp.py
+++ b/ecnet/models/mlp.py
@@ -2,184 +2,215 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/models/mlp.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains the "MultilayerPerceptron" (feed-forward neural network) class
 #
 
 # Stdlib imports
+from os import environ
 from re import compile, IGNORECASE
-from os import devnull, environ
-import sys
 
 # 3rd party imports
-from tensorflow import get_default_graph, logging
-from numpy import array
-stderr = sys.stderr
-sys.stderr = open(devnull, 'w')
-from keras.backend import clear_session, reset_uids
-from keras.callbacks import EarlyStopping
-from keras.layers import Dense
-from keras.losses import mean_squared_error
-from keras.metrics import mae
-from keras.models import load_model, Sequential
-from keras.optimizers import Adam
-sys.stderr = stderr
+from h5py import File
+from numpy import array, string_, zeros
+from tensorflow import config, Tensor
+from tensorflow.keras.callbacks import EarlyStopping
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.losses import MeanSquaredError
+from tensorflow.keras.models import Model
+from tensorflow.keras.optimizers import Adam
 
 # ECNet imports
 from ecnet.utils.logging import logger
 
 environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
-logging.set_verbosity(logging.ERROR)
-
+config.experimental_run_functions_eagerly(True)
 H5_EXT = compile(r'.*\.h5', flags=IGNORECASE)
 
 
-class MultilayerPerceptron:
+def check_h5(filename: str):
+    ''' Ensures a given filename has an `.h5` extension
+
+    Args:
+        filename (str): filename to check
+    '''
+
+    if H5_EXT.match(filename) is None:
+        raise ValueError(
+            'Invalid filename/extension, must be `.h5`: {}'.format(
+                filename
+            )
+        )
+
+
+class MultilayerPerceptron(Model):
 
-    def __init__(self, filename: str='model.h5'):
-        '''MultilayerPerceptron object: fits neural network to supplied inputs
-        and targets
+    def __init__(self, filename: str = 'model.h5'):
+        ''' MultilayerPerceptron: Feed-forward neural network; variable number
+        of layers, variable size/activations of layers; handles training,
+        saving/loading of models
 
         Args:
-            filename (str): path to model save location (.h5 extension)
+            filename (str): filename/path for the model (default: `model.h5`)
         '''
 
-        if H5_EXT.match(filename) is None:
-            raise ValueError(
-                'Invalid filename/extension, must be `.h5`: {}'.format(
-                    filename
-                )
-            )
+        super(MultilayerPerceptron, self).__init__()
+        check_h5(filename)
         self._filename = filename
-        clear_session()
-        self._model = Sequential(name=filename.lower().replace('.h5', ''))
+        self._layers = []
 
     def add_layer(self, num_neurons: int, activation: str,
-                  input_dim: int=None):
-        '''Adds a fully-connected layer to the model
+                  input_dim: int = None):
+        ''' add_layer: adds a layer to the MLP; layers are added sequentially;
+        first layer must have input dimensionality specified
 
         Args:
-            num_neurons (int): number of neurons for the layer
-            activation (str): activation function for the layer (see Keras
-                activation function documentation)
-            input_dim (int): if not None (input layer), specifies input
-                dimensionality
+            num_neurons (int): number of neurons in the layer
+            activation (str): activation function used by the layer; refer to
+               https://www.tensorflow.org/api_docs/python/tf/keras/activations
+               for usable activation functions
+            input_dim (int): specify input dimensionality for the first layer;
+               all other layers depend on previous layers' size (number of
+               neurons), should be kept as `None` (default value)
         '''
 
-        self._model.add(Dense(
+        if len(self._layers) == 0:
+            if input_dim is None:
+                raise ValueError('First layer must have input_dim specified')
+
+        self._layers.append(Dense(
             units=num_neurons,
             activation=activation,
             input_shape=(input_dim,)
         ))
 
-    def fit(self, l_x: array, l_y: array, v_x: array=None, v_y: array=None,
-            epochs: int=1500, lr: float=0.001, beta_1: float=0.9,
-            beta_2: float=0.999, epsilon: float=0.0000001, decay: float=0.0,
-            v: int=0, batch_size: int=32):
-        '''Fits neural network to supplied inputs and targets
+    def call(self, x: Tensor) -> Tensor:
+        ''' call: used by Model.fit (parent) to perform feed-forward operations
 
         Args:
-            l_x (numpy.array): learning input data
-            l_y (numpy.array): learning target data
-            v_x (numpy.array): if not None, periodic validation is performed w/
-                these inputs
-            v_y (numpy.array): if not None, periodic validation is performed w/
-                these targets
-            epochs (int): number of learning epochs if not validating, maximum
-                number of learning epochs if performing periodic validation
-            lr (float): learning rate for Adam optimizer
-            beta_1 (float): beta_1 value for Adam optimizer
-            beta_2 (float): beta_2 value for Adam optimizer
-            epsilon (float): epsilon value for Adam optimizer
-            decay (float): learning rate decay for Adam optimizer
-            v (int): verbose training, `0` for no printing, `1` for printing
-            batch_size (int): number of learning samples per batch
+            x (tf.Tensor): data fed into first layer
+
+        Returns:
+            tf.Tensor: data resulting from last layer
         '''
 
-        self._model.compile(
-            loss=mean_squared_error,
-            optimizer=Adam(
-                lr=lr,
-                beta_1=beta_1,
-                beta_2=beta_2,
-                epsilon=epsilon,
-                decay=decay
-            ),
-            metrics=[mae]
-        )
+        for layer in self._layers:
+            x = layer(x)
+        return x
+
+    def fit(self, l_x: array, l_y: array, v_x: array = None, v_y: array = None,
+            epochs: int = 1500, lr: float = 0.001, beta_1: float = 0.9,
+            beta_2: float = 0.999, epsilon: float = 0.0000001,
+            decay: float = 0.0, v: int = 0, batch_size: int = 32,
+            patience: int = 128) -> tuple:
+        ''' fit: trains model using supplied data; may supply additional data
+        to use as validation set (determines learning cutoff); hyperparameters
+        for Adam optimization function, batch size, patience (if validating)
+        may be specified
+
+        Args:
+            l_x (np.array): learning input data; each sub-iterable is a sample
+            l_y (np.array): learning target data; each sub-iterable is a sample
+            v_x (np.array): validation input data (`None` for no validation)
+            v_y (np.array): validation target data (`None` for no validation)
+            epochs (int): number of training iterations, max iterations if
+                performing validation
+            lr (float): learning rate of Adam optimization fn
+            beta_1 (float): first moment estimate of Adam optimization fn
+            beta_2 (float): second moment estimate of Adam optimization fn
+            epsilon (float): number to prevent division by zero in Adam fn
+            decay (float): decay of learning date in Adam optimization fn
+            v (int): whether Model.fit (parent) is verbose (1 True, 0 False)
+            batch_size (int): size of each training batch
+            patience (int): maximum number of epochs to wait before better
+                validation loss found; if not found, training terminates, best
+                weights restored
+
+        Returns:
+            tuple: (list: learn losses, list: valid losses); each list equal
+                length; each list element represents loss at corresponding
+                epoch
+        '''
+
+        self.compile(optimizer=Adam(lr=lr, beta_1=beta_1, beta_2=beta_2,
+                                    epsilon=epsilon,
+                                    decay=decay),
+                     loss=MeanSquaredError())
 
         if v_x is not None and v_y is not None:
-            history = self._model.fit(
-                l_x,
-                l_y,
-                validation_data=(v_x, v_y),
-                callbacks=[EarlyStopping(
-                    monitor='val_loss',
-                    patience=250,
-                    verbose=v,
-                    mode='min',
-                    restore_best_weights=True
-                )],
-                epochs=epochs,
-                verbose=v,
-                batch_size=batch_size
-            )
-            epochs = len(history.history['loss'])
+
+            callback = EarlyStopping(monitor='val_loss', patience=patience,
+                                     restore_best_weights=True)
+            history = super().fit(l_x, l_y, batch_size=batch_size,
+                                  epochs=epochs, verbose=v,
+                                  callbacks=[callback],
+                                  validation_data=(v_x, v_y))
+            return (history.history['loss'], history.history['val_loss'])
+
         else:
-            self._model.fit(
-                l_x,
-                l_y,
-                epochs=epochs,
-                verbose=v,
-                batch_size=batch_size
-            )
 
-        logger.log('debug', 'Training complete after {} epochs'.format(epochs),
-                   call_loc='MLP')
+            history = super().fit(l_x, l_y, batch_size=batch_size,
+                                  epochs=epochs, verbose=v)
+            return (history.history['loss'], [None for _ in range(epochs)])
 
     def use(self, x: array) -> array:
-        '''Uses neural network to predict values for supplied data
+        ''' use: uses the model to predict values for supplied data
 
         Args:
-            x (numpy.array): input data to predict for
+            x (np.array): input data to predict for
 
-        Returns
-            numpy.array: predictions
+        Returns:
+            np.array: predicted values
         '''
 
-        with get_default_graph().as_default():
-            return self._model.predict(x)
+        return self.predict(x)
 
-    def save(self, filename: str=None):
-        '''Saves neural network to .h5 file
+    def save(self, filename: str = None):
+        ''' save: saves the model weights, architecture to either the filename/
+        path specified when object was created, or new, supplied filename/path
 
-        filename (str): if None, uses MultilayerPerceptron._filename;
-            otherwise, saves to this file
+        Args:
+            filename (str): new filepath if different than init filename/path
         '''
 
         if filename is None:
             filename = self._filename
-        if H5_EXT.match(filename) is None:
-            raise ValueError(
-                'Invalid filename/extension, must be `.h5`: {}'.format(
-                    filename
-                )
-            )
-        self._model.save(filename)
+        check_h5(filename)
+        self.save_weights(filename, save_format='h5')
+        input_size = self.layers[0].get_config()['batch_input_shape'][1]
+        layer_sizes = [l.get_config()['units'] for l in self.layers]
+        layer_activ = [l.get_config()['activation'] for l in self.layers]
+        with File(filename, 'a') as hf:
+            hf['mlp_input_size'] = input_size
+            hf['mlp_layer_sizes'] = layer_sizes
+            hf['mlp_layer_activ'] = string_(layer_activ)
+        hf.close()
         logger.log('debug', 'Model saved to {}'.format(filename),
                    call_loc='MLP')
 
-    def load(self, filename: str=None):
-        '''Loads neural network from .h5 file
+    def load(self, filename: str = None):
+        ''' load: loads a saved model, restoring the architecture/weights;
+        loads from filename/path specified during object initialization,
+        unless new filename/path specified
 
         Args:
-            filename (str): path to .h5 model file
+            filename (str): new filepath if different than init filename/path
         '''
 
         if filename is None:
             filename = self._filename
-        self._model = load_model(filename)
+        with File(filename, 'r') as hf:
+            input_size = hf.get('mlp_input_size').value
+            layer_sizes = hf.get('mlp_layer_sizes').value
+            layer_activ = hf.get('mlp_layer_activ').value
+        hf.close()
+        self.add_layer(layer_sizes[0], layer_activ[0].decode('ascii'),
+                       input_size)
+        for idx, layer in enumerate(layer_sizes[1:]):
+            self.add_layer(layer, layer_activ[idx].decode('ascii'))
+        self.build(input_shape=(None, input_size))
+        self.load_weights(filename)
         logger.log('debug', 'Model loaded from {}'.format(filename),
                    call_loc='MLP')
diff --git a/ecnet/server.py b/ecnet/server.py
index 226ae55..c72c1b0 100644
--- a/ecnet/server.py
+++ b/ecnet/server.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/server.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains the "Server" class, which handles ECNet project creation, neural
 # network model creation, data hand-off to models, prediction error
@@ -26,8 +26,8 @@
 
 class Server:
 
-    def __init__(self, model_config: str='config.yml', prj_file: str=None,
-                 num_processes: int=1):
+    def __init__(self, model_config: str = 'config.yml', prj_file: str = None,
+                 num_processes: int = 1):
         '''Server object: handles data loading, model creation, data-to-model
         hand-off, data input parameter selection, hyperparameter tuning
 
@@ -69,8 +69,8 @@ def __init__(self, model_config: str='config.yml', prj_file: str=None,
             self._vars = default_config()
             save_config(self._vars, self._cf_file)
 
-    def load_data(self, filename: str, random: bool=False, split: list=None,
-                  normalize: bool=False):
+    def load_data(self, filename: str, random: bool = False,
+                  split: list = None, normalize: bool = False):
         '''Loads data from an ECNet-formatted CSV database
 
         Args:
@@ -90,8 +90,8 @@ def load_data(self, filename: str, random: bool=False, split: list=None,
         self._df.create_sets(random, split)
         self._sets = self._df.package_sets()
 
-    def create_project(self, project_name: str, num_pools: int=1,
-                       num_candidates: int=1):
+    def create_project(self, project_name: str, num_pools: int = 1,
+                       num_candidates: int = 1):
         '''Creates folder hierarchy for a new project
 
         Args:
@@ -111,8 +111,9 @@ def create_project(self, project_name: str, num_pools: int=1,
         logger.log('debug', 'Number of candidates/pool: {}'.format(
                    num_candidates), call_loc='PROJECT')
 
-    def limit_inputs(self, limit_num: int, num_estimators: int=None,
-                     output_filename: str=None, **kwargs) -> list:
+    def limit_inputs(self, limit_num: int, num_estimators: int = None,
+                     eval_set: str = 'learn', output_filename: str = None,
+                     **kwargs) -> list:
         '''Selects `limit_num` influential input parameters using random
         forest regression
 
@@ -122,6 +123,8 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None,
                 defaults to the total number of inputs
             output_filename (str): if not None, new limited database is saved
                 here
+            eval_set (str): set to perform RFR on (`learn`, `valid`, `train`,
+                `test`, None (all)) (default: `learn`)
             **kwargs: any argument accepted by
                 sklearn.ensemble.RandomForestRegressor
 
@@ -133,7 +136,9 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None,
             self._df,
             limit_num,
             num_estimators,
-            self._num_processes
+            self._num_processes,
+            eval_set,
+            **kwargs
         )
         self._df.set_inputs([r[0] for r in result])
         self._sets = self._df.package_sets()
@@ -144,9 +149,9 @@ def limit_inputs(self, limit_num: int, num_estimators: int=None,
         return result
 
     def tune_hyperparameters(self, num_employers: int, num_iterations: int,
-                             shuffle: bool=None, split: list=None,
-                             validate: bool=True, eval_set: str=None,
-                             eval_fn: str='rmse'):
+                             shuffle: bool = None, split: list = None,
+                             validate: bool = True, eval_set: str = None,
+                             eval_fn: str = 'rmse', epochs: int = 300):
         '''Tunes neural network learning hyperparameters using an artificial
         bee colony algorithm; tuned hyperparameters are saved to Server's
         model configuration file
@@ -162,6 +167,7 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int,
                 `train`, `test`, None (all sets)
             eval_fn (str): error function used to evaluate bee fitness;
                 `rmse`, `mean_abs_error`, `med_abs_error`
+            epochs (int): number of training epochs per bee ANN (def: 300)
         '''
 
         self._vars = tune_hyperparameters(
@@ -174,13 +180,15 @@ def tune_hyperparameters(self, num_employers: int, num_iterations: int,
             split,
             validate,
             eval_set,
-            eval_fn
+            eval_fn,
+            epochs
         )
         save_config(self._vars, self._cf_file)
 
-    def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
-              validate: bool=False, selection_set: str=None,
-              selection_fn: str='rmse'):
+    def train(self, shuffle: str = None, split: list = None,
+              retrain: bool = False, validate: bool = False,
+              selection_set: str = None, selection_fn: str = 'rmse',
+              model_filename: str = 'model.h5', verbose: int = 0) -> tuple:
         '''Trains neural network(s) using currently-loaded data; single NN if
         no project is created, all candidates if created
 
@@ -196,18 +204,29 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
                 set; `learn`, `valid`, `train`, `test`, None (all data)
             selection_fn (str): candidates are selected based on this error
                 metric; `rmse`, `mean_abs_error`, `med_abs_error`
+            model_filename (str): if project not created, saves `.h5` file
+                here
+            verbose (int): 1 to display loss at each epoch, 0 otherwise (single
+                model only)
+
+        Returns:
+            tuple: if training single model, returns tuple of learn/valid
+                losses, else None
         '''
 
         if self._prj_name is None:
             logger.log('info', 'Training single model', call_loc='TRAIN')
-            train_model(
+            _, losses = train_model(
                 self._sets,
                 self._vars,
                 selection_set,
                 selection_fn,
                 retrain,
-                validate=validate
+                model_filename,
+                validate,
+                verbose=verbose
             )
+            return losses
 
         else:
             train_project(
@@ -225,8 +244,10 @@ def train(self, shuffle: str=None, split: list=None, retrain: bool=False,
                 selection_fn,
                 self._num_processes
             )
+            return None
 
-    def use(self, dset: str=None, output_filename: str=None) -> list:
+    def use(self, dset: str = None, output_filename: str = None,
+            model_filename: str = 'model.h5') -> list:
         '''Uses trained neural network(s) to predict for specified set; single
         NN if no project created, best pool candidates if created
 
@@ -234,13 +255,14 @@ def use(self, dset: str=None, output_filename: str=None) -> list:
             dset (str): set to predict for; `learn`, `valid`, `train`, `test`,
                 None (all sets)
             output_filename (str): if supplied, saves results to this CSV file
+            model_filename (str): if supplied, use specified .h5 model file
 
         Returns:
             list: list of results for specified set
         '''
 
         if self._prj_name is None:
-            results = use_model(self._sets, dset)
+            results = use_model(self._sets, dset, model_filename)
 
         else:
             results = use_project(
@@ -255,7 +277,8 @@ def use(self, dset: str=None, output_filename: str=None) -> list:
                        call_loc='USE')
         return results
 
-    def errors(self, *args, dset: str=None) -> dict:
+    def errors(self, *args, dset: str = None,
+               model_filename: str = 'model.h5') -> dict:
         '''Obtains various errors for specified set
 
         Args:
@@ -263,6 +286,8 @@ def errors(self, *args, dset: str=None) -> dict:
                 `med_abs_error`, `r2`
             dset (str): set to obtain errors for; `learn`, `valid`, `train`,
                 `test`, None (all sets)
+            model_filename (str): if specified, uses .h5 model file for error
+                calculations
 
         Returns:
             dict: {'error_fn', value ...} with supplied errors
@@ -271,7 +296,7 @@ def errors(self, *args, dset: str=None) -> dict:
         for err in args:
             logger.log('debug', 'Calculating {} for {} set'.format(err, dset),
                        call_loc='ERRORS')
-        preds = self.use(dset)
+        preds = self.use(dset, model_filename=model_filename)
         y_vals = get_y(self._sets, dset)
         errors = {}
         for err in args:
@@ -279,8 +304,8 @@ def errors(self, *args, dset: str=None) -> dict:
         logger.log('debug', 'Errors: {}'.format(errors), call_loc='ERRORS')
         return errors
 
-    def save_project(self, filename: str=None, clean_up: bool=True,
-                     del_candidates: bool=False):
+    def save_project(self, filename: str = None, clean_up: bool = True,
+                     del_candidates: bool = False):
         '''Saves current state of project to a .prj file
 
         Args:
diff --git a/ecnet/tasks/limit_inputs.py b/ecnet/tasks/limit_inputs.py
index 3902730..48b1d74 100644
--- a/ecnet/tasks/limit_inputs.py
+++ b/ecnet/tasks/limit_inputs.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/limit_inputs.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions for selecting influential input parameters
 #
@@ -18,10 +18,12 @@
 # ECNet imports
 from ecnet.utils.data_utils import DataFrame
 from ecnet.utils.logging import logger
+from ecnet.utils.server_utils import get_x, get_y
 
 
-def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None,
-                  num_processes: int=1, **kwargs) -> list:
+def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int = None,
+                  num_processes: int = 1, eval_set: str = 'learn',
+                  **kwargs) -> list:
     '''Uses random forest regression to select input parameters
 
     Args:
@@ -29,6 +31,8 @@ def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None,
         limit_num (int): desired number of input parameters
         num_estimators (int): number of trees used by RFR algorithm
         num_processes (int): number of parallel jobs for RFR algorithm
+        eval_set (str): set to perform RFR on (`learn`, `valid`, `train`,
+            `test`, None (all)) (default: `learn`)
         **kwargs: any argument accepted by
             sklearn.ensemble.RandomForestRegressor
 
@@ -40,15 +44,8 @@ def limit_rforest(df: DataFrame, limit_num: int, num_estimators: int=None,
                .format(limit_num), call_loc='LIMIT')
 
     pd = df.package_sets()
-    X = pd.learn_x
-    y = pd.learn_y
-    if len(pd.valid_x) > 0:
-        X = concatenate((X, pd.valid_x))
-        y = concatenate((y, pd.valid_y))
-    if len(pd.test_x) > 0:
-        X = concatenate((X, pd.test_x))
-        y = concatenate((y, pd.test_y))
-    y = ravel(y)
+    X = get_x(pd, eval_set)
+    y = ravel(get_y(pd, eval_set))
 
     if num_estimators is None:
         num_estimators = len(X[0])
diff --git a/ecnet/tasks/training.py b/ecnet/tasks/training.py
index 606054d..252b14f 100644
--- a/ecnet/tasks/training.py
+++ b/ecnet/tasks/training.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/training.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains function for project training (multiprocessed training)
 #
@@ -83,7 +83,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int,
                 pool_errors[pool].append(train_model(
                     sets, vars, selection_set, selection_fn, retrain, filename,
                     validate
-                ))
+                )[0])
 
             if shuffle is not None:
                 df.shuffle(sets=shuffle, split=split)
@@ -93,7 +93,7 @@ def train_project(prj_name: str, num_pools: int, num_candidates: int,
         train_pool.close()
         train_pool.join()
         for p_idx, pool in enumerate(pool_errors):
-            pool_errors[p_idx] = [e.get() for e in pool]
+            pool_errors[p_idx] = [e.get()[0] for e in pool]
 
     logger.log('debug', 'Pool errors: {}'.format(pool_errors),
                call_loc='TRAIN')
diff --git a/ecnet/tasks/tuning.py b/ecnet/tasks/tuning.py
index dbc41ee..3792a6d 100644
--- a/ecnet/tasks/tuning.py
+++ b/ecnet/tasks/tuning.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tasks/tuning.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions/fitness functions for tuning hyperparameters
 #
@@ -22,10 +22,10 @@
 
 
 def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
-                         num_iterations: int, num_processes: int=1,
-                         shuffle: str=None, split: list=None,
-                         validate: bool=True, eval_set: str=None,
-                         eval_fn: str='rmse') -> dict:
+                         num_iterations: int, num_processes: int = 1,
+                         shuffle: str = None, split: list = None,
+                         validate: bool = True, eval_set: str = None,
+                         eval_fn: str = 'rmse', epochs: int = 300) -> dict:
     '''Tunes neural network learning/architecture hyperparameters
 
     Args:
@@ -41,6 +41,7 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
             `train`, `test`, None (all sets)
         eval_fn (str): error function used to evaluate bee performance; `rmse`,
             `mean_abs_error`, `med_abs_error`
+        epochs (int): number of training epochs per bee ANN (def: 300)
 
     Returns:
         dict: tuned hyperparameters
@@ -67,20 +68,19 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
         'validate': validate,
         'eval_set': eval_set,
         'eval_fn': eval_fn,
-        'hidden_layers': vars['hidden_layers']
+        'hidden_layers': vars['hidden_layers'],
+        'epochs': epochs
     }
 
     value_ranges = [
-        ('float', (0.0, 1.0)),
-        ('float', (0.0, 1.0)),
-        ('float', (0.0, 1.0)),
-        ('float', (0.0, 1.0)),
-        ('float', (0.0, 1.0)),
-        ('int', (1, len(df.learn_set)))
+        ('float', (1e-9, 1e-4)),            # Learning rate decay
+        ('float', (1e-5, 0.1)),             # Learning rate
+        ('int', (1, len(df.learn_set))),    # Batch size
+        ('int', (64, 1024))                 # Patience
     ]
 
     for _ in range(len(vars['hidden_layers'])):
-        value_ranges.append(('int', (1, 100)))
+        value_ranges.append(('int', (1, len(df._input_names))))
 
     abc = ABC(
         tune_fitness_function,
@@ -101,24 +101,20 @@ def tune_hyperparameters(df: DataFrame, vars: dict, num_employers: int,
         abc.run_iteration()
         logger.log('info', 'Best Performer: {}, {}'.format(
             abc.best_performer[2], {
-                'beta_1': abc.best_performer[1][0],
-                'beta_2': abc.best_performer[1][1],
-                'decay': abc.best_performer[1][2],
-                'epsilon': abc.best_performer[1][3],
-                'learning_rate': abc.best_performer[1][4],
-                'batch_size': abc.best_performer[1][5],
-                'hidden_layers': abc.best_performer[1][6:]
+                'decay': abc.best_performer[1][0],
+                'learning_rate': abc.best_performer[1][1],
+                'batch_size': abc.best_performer[1][2],
+                'patience': abc.best_performer[1][3],
+                'hidden_layers': abc.best_performer[1][4:]
             }
         ), call_loc='TUNE')
     params = abc.best_performer[1]
-    vars['beta_1'] = params[0]
-    vars['beta_2'] = params[1]
-    vars['decay'] = params[2]
-    vars['epsilon'] = params[3]
-    vars['learning_rate'] = params[4]
-    vars['batch_size'] = params[5]
+    vars['decay'] = params[0]
+    vars['learning_rate'] = params[1]
+    vars['batch_size'] = params[2]
+    vars['patience'] = params[3]
     for l_idx in range(len(vars['hidden_layers'])):
-        vars['hidden_layers'][l_idx][0] = params[6 + l_idx]
+        vars['hidden_layers'][l_idx][0] = params[4 + l_idx]
     return vars
 
 
@@ -134,16 +130,14 @@ def tune_fitness_function(params: dict, **kwargs):
     '''
 
     vars = default_config()
-    vars['beta_1'] = params[0]
-    vars['beta_2'] = params[1]
-    vars['decay'] = params[2]
-    vars['epsilon'] = params[3]
-    vars['learning_rate'] = params[4]
-    vars['batch_size'] = params[5]
+    vars['decay'] = params[0]
+    vars['learning_rate'] = params[1]
+    vars['batch_size'] = params[2]
+    vars['patience'] = params[3]
     vars['hidden_layers'] = kwargs['hidden_layers']
-    vars['epochs'] = 3000
+    vars['epochs'] = kwargs['epochs']
     for l_idx in range(len(vars['hidden_layers'])):
-        vars['hidden_layers'][l_idx][0] = params[6 + l_idx]
+        vars['hidden_layers'][l_idx][0] = params[4 + l_idx]
 
     df = kwargs['df']
     if kwargs['shuffle'] is not None:
@@ -151,4 +145,4 @@ def tune_fitness_function(params: dict, **kwargs):
     sets = df.package_sets()
 
     return train_model(sets, vars, kwargs['eval_set'], kwargs['eval_fn'],
-                       validate=kwargs['validate'], save=False)
+                       validate=kwargs['validate'], save=False)[0]
diff --git a/ecnet/tools/database.py b/ecnet/tools/database.py
index 5385481..4be7e76 100644
--- a/ecnet/tools/database.py
+++ b/ecnet/tools/database.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/database.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions for creating ECNet-formatted databases
 #
@@ -20,7 +20,7 @@
 
 try:
     import pybel
-except:
+except ImportError:
     pybel = None
 
 
@@ -35,9 +35,9 @@ def __init__(self, id):
         self.inputs = None
 
 
-def create_db(smiles: list, db_name: str, targets: list=None,
-              id_prefix: str='', extra_strings: dict={}, backend: str='padel',
-              convert_mdl: bool=False):
+def create_db(smiles: list, db_name: str, targets: list = None,
+              id_prefix: str = '', extra_strings: dict = {},
+              backend: str = 'padel', convert_mdl: bool = False):
     ''' create_db: creates an ECNet-formatted database from SMILES strings
     using either PaDEL-Descriptor or alvaDesc software; using alvaDesc
     requires a valid installation/license of alvaDesc
diff --git a/ecnet/tools/plotting.py b/ecnet/tools/plotting.py
index 441db81..895aafb 100644
--- a/ecnet/tools/plotting.py
+++ b/ecnet/tools/plotting.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/plotting.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions/classes for creating various plots
 #
@@ -18,9 +18,10 @@
 
 class ParityPlot:
 
-    def __init__(self, title: str='Parity Plot',
-                 x_label: str='Experimental Value',
-                 y_label: str='Predicted Value', font: str='Times New Roman'):
+    def __init__(self, title: str = 'Parity Plot',
+                 x_label: str = 'Experimental Value',
+                 y_label: str = 'Predicted Value',
+                 font: str = 'Times New Roman'):
         ''' ParityPlot: creates a plot of predicted values vs. experimental
         data relative to a 1:1 parity line
 
@@ -39,7 +40,7 @@ def __init__(self, title: str='Parity Plot',
         self._min_val = 0
         self._labels = None
 
-    def add_series(self, x_vals, y_vals, name: str=None, color: str=None):
+    def add_series(self, x_vals, y_vals, name: str = None, color: str = None):
         ''' Adds data to the plot
 
         Args:
@@ -67,7 +68,7 @@ def add_series(self, x_vals, y_vals, name: str=None, color: str=None):
         if y_min < self._min_val:
             self._min_val = y_min
 
-    def add_error_bars(self, error: float, label: str=None):
+    def add_error_bars(self, error: float, label: str = None):
         ''' Adds error bars, +/- the error relative to the 1:1 parity line
 
         Args:
@@ -96,7 +97,7 @@ def save(self, filename: str):
         self._add_parity_line()
         plt.savefig(filename)
 
-    def _add_parity_line(self, offset: float=0.0):
+    def _add_parity_line(self, offset: float = 0.0):
         ''' Adds a 1:1 parity line
 
         Args:
diff --git a/ecnet/tools/project.py b/ecnet/tools/project.py
index b09a7d2..ba00b57 100644
--- a/ecnet/tools/project.py
+++ b/ecnet/tools/project.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/tools/project.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions for predicting data using pre-existing .prj files
 #
@@ -20,8 +20,8 @@
 from ecnet.tools.database import create_db
 
 
-def predict(smiles: list, prj_file: str, results_file: str=None,
-            backend: str='padel') -> list:
+def predict(smiles: list, prj_file: str, results_file: str = None,
+            backend: str = 'padel') -> list:
     ''' predict: predicts values for supplied molecules (SMILES strings) using
     pre-existing ECNet project (.prj) file
 
diff --git a/ecnet/utils/data_utils.py b/ecnet/utils/data_utils.py
index bbdee25..0a32407 100644
--- a/ecnet/utils/data_utils.py
+++ b/ecnet/utils/data_utils.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/data_utils.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions/classes for loading data, saving data, saving results
 #
@@ -33,8 +33,8 @@ def __init__(self):
 class PackagedData:
 
     def __init__(self):
-        '''PackagedData object: contains lists of input and target data for data
-        set assignments
+        '''PackagedData object: contains lists of input and target data for
+        data set assignments
         '''
 
         self.learn_x = []
@@ -58,7 +58,7 @@ def __init__(self, filename: str):
         if '.csv' not in filename:
             filename += '.csv'
         try:
-            with open(filename, newline='') as file:
+            with open(filename, newline='', encoding='utf8') as file:
                 rows = list(reader(file))
         except FileNotFoundError:
             raise Exception('CSV database not found: {}'.format(filename))
@@ -112,7 +112,7 @@ def __len__(self):
 
         return len(self.data_points)
 
-    def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]):
+    def create_sets(self, random: bool = False, split: list = [0.7, 0.2, 0.1]):
         '''Creates learning, validation and test sets
 
         Args:
@@ -167,9 +167,9 @@ def create_sets(self, random: bool=False, split: list=[0.7, 0.2, 0.1]):
         logger.log('debug', 'Number of entries in test set: {}'.format(
                    len(self.test_set)), call_loc='DF')
 
-    def create_sorted_sets(self, sort_str: str, split: list=[0.7, 0.2, 0.1]):
-        '''Creates random learn, validate and test sets, ensuring data points with
-        the supplied sort string are split proportionally between the sets
+    def create_sorted_sets(self, sort_str: str, split: list = [0.7, 0.2, 0.1]):
+        '''Creates random learn, validate and test sets, ensuring data points
+        with the supplied sort string are split proportionally between the sets
 
         Args:
             sort_str (str): database STRING value used to sort data points
@@ -239,7 +239,7 @@ def normalize(self):
                         (float(getattr(pt, inp)) - v_min) / (v_max - v_min)
                     )
 
-    def shuffle(self, sets: str='all', split: list=[0.7, 0.2, 0.1]):
+    def shuffle(self, sets: str = 'all', split: list = [0.7, 0.2, 0.1]):
         '''Shuffles learning, validation and test sets or learning and
         validation sets
 
@@ -371,7 +371,7 @@ def save(self, filename: str):
             data_rows.append(data_row)
         rows.extend(sorted(data_rows, key=lambda x: x[0]))
 
-        with open(filename, 'w') as csv_file:
+        with open(filename, 'w', encoding='utf8') as csv_file:
             wr = writer(csv_file, quoting=QUOTE_ALL, lineterminator='\n')
             for row in rows:
                 wr.writerow(row)
@@ -437,7 +437,7 @@ def save_results(results: list, dset: str, df: DataFrame, filename: str):
         data_rows.append(data_row)
     rows.extend(sorted(data_rows, key=lambda x: x[0]))
 
-    with open(filename, 'w') as csv_file:
+    with open(filename, 'w', encoding='utf8') as csv_file:
         wr = writer(csv_file, quoting=QUOTE_ALL, lineterminator='\n')
         for row in rows:
             wr.writerow(row)
diff --git a/ecnet/utils/error_utils.py b/ecnet/utils/error_utils.py
index 4546c39..16cbf1b 100644
--- a/ecnet/utils/error_utils.py
+++ b/ecnet/utils/error_utils.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/error_utils.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <Travis_Kessler@student.uml.edu>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <Travis_Kessler@student.uml.edu>
 #
 # Contains functions for error calculations
 #
diff --git a/ecnet/utils/logging.py b/ecnet/utils/logging.py
index f4cc13d..32798bc 100644
--- a/ecnet/utils/logging.py
+++ b/ecnet/utils/logging.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/logging.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <Travis_Kessler@student.uml.edu>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <Travis_Kessler@student.uml.edu>
 #
 # Contains logger used by ECNet
 #
diff --git a/ecnet/utils/server_utils.py b/ecnet/utils/server_utils.py
index 12ec84f..4d2b1f7 100644
--- a/ecnet/utils/server_utils.py
+++ b/ecnet/utils/server_utils.py
@@ -2,8 +2,8 @@
 # -*- coding: utf-8 -*-
 #
 # ecnet/utils/server_utils.py
-# v.3.2.3
-# Developed in 2019 by Travis Kessler <travis.j.kessler@gmail.com>
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
 #
 # Contains functions used by ecnet.Server
 #
@@ -65,22 +65,23 @@ def default_config() -> dict:
 
     return {
         'epochs': 3000,
-        'learning_rate': 0.001,
+        'learning_rate': 0.01,
         'beta_1': 0.9,
         'beta_2': 0.999,
-        'epsilon': 0.0000001,
+        'epsilon': 1e-8,
         'decay': 0.0,
         'hidden_layers': [
             [32, 'relu'],
             [32, 'relu']
         ],
         'output_activation': 'linear',
-        'batch_size': 32
+        'batch_size': 32,
+        'patience': 128
     }
 
 
-def get_candidate_path(prj: str, pool: int, candidate: int=None,
-                       model: bool=False, p_best: bool=False) -> str:
+def get_candidate_path(prj: str, pool: int, candidate: int = None,
+                       model: bool = False, p_best: bool = False) -> str:
     '''Get path to various states of model.h5 files
 
     Args:
@@ -366,8 +367,9 @@ def save_project(prj_name: str, filename: str, config_filename: str,
 
 
 def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str,
-                retrain: bool=False, filename: str='model.h5',
-                validate: bool=True, save: bool=True) -> float:
+                retrain: bool = False, filename: str = 'model.h5',
+                validate: bool = True, save: bool = True,
+                verbose: int = 0) -> tuple:
     '''Trains neural network
 
     Args:
@@ -380,9 +382,11 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str,
         retrain (bool): if True, opens model for additional training
         filename (str): path to .h5 model file, loads from this if retraining
         save (bool): if True, saves model to `filename`
+        verbose (int): 1 to display loss at each epoch, 0 otherwise (single
+                model only)
 
     Returns:
-        float: error of evaluated set
+        tuple: (error of evaluated set, tuple of learn/valid losses)
     '''
 
     model = MultilayerPerceptron(filename=filename)
@@ -396,7 +400,7 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str,
                 model.add_layer(layer[0], layer[1])
         model.add_layer(len(sets.learn_y[0]), vars['output_activation'])
     if validate:
-        model.fit(
+        losses = model.fit(
             sets.learn_x,
             sets.learn_y,
             sets.valid_x,
@@ -407,10 +411,12 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str,
             beta_2=vars['beta_2'],
             epsilon=vars['epsilon'],
             decay=vars['decay'],
-            batch_size=vars['batch_size']
+            batch_size=vars['batch_size'],
+            v=verbose,
+            patience=vars['patience']
         )
     else:
-        model.fit(
+        losses = model.fit(
             sets.learn_x,
             sets.learn_y,
             epochs=vars['epochs'],
@@ -419,19 +425,21 @@ def train_model(sets: PackagedData, vars: dict, eval_set: str, eval_fn: str,
             beta_2=vars['beta_2'],
             epsilon=vars['epsilon'],
             decay=vars['decay'],
-            batch_size=vars['batch_size']
+            batch_size=vars['batch_size'],
+            v=verbose,
+            patience=vars['patience']
         )
     if save:
         model.save()
-    return get_error(
+    return (get_error(
         model.use(get_x(sets, eval_set)),
         get_y(sets, eval_set),
         eval_fn
-    )
+    ), losses)
 
 
 def use_model(sets: PackagedData, dset: str,
-              filename: str='model.h5') -> array:
+              filename: str = 'model.h5') -> array:
     '''Uses existing model to predict data
 
     Args:
diff --git a/ecnet/workflows/__init__.py b/ecnet/workflows/__init__.py
new file mode 100644
index 0000000..52dfc8c
--- /dev/null
+++ b/ecnet/workflows/__init__.py
@@ -0,0 +1,2 @@
+import ecnet.workflows.ecrl_workflow
+import ecnet.workflows.workflow_utils
diff --git a/ecnet/workflows/ecrl_workflow.py b/ecnet/workflows/ecrl_workflow.py
new file mode 100644
index 0000000..c85e0ac
--- /dev/null
+++ b/ecnet/workflows/ecrl_workflow.py
@@ -0,0 +1,174 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# ecnet/workflows/ecrl_workflow.py
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
+#
+# General workflow used by the UMass Lowell Energy and Combustion Research
+# Laboratory
+#
+
+# Stdlib imports
+from datetime import datetime
+
+# 3rd party imports
+from matplotlib import pyplot as plt
+
+# ECNet imports
+from ecnet import Server
+from ecnet.tasks.tuning import tune_hyperparameters
+from ecnet.tools.database import create_db
+from ecnet.tools.plotting import ParityPlot
+from ecnet.utils.data_utils import DataFrame
+from ecnet.utils.logging import logger
+from ecnet.utils.server_utils import default_config, save_config
+from ecnet.workflows.workflow_utils import find_optimal_num_inputs,\
+    prop_range_from_split
+
+
+def create_model(prop_abvr: str, smiles: list = None, targets: list = None,
+                 db_name: str = None, qspr_backend: str = 'padel',
+                 create_plots: bool = True, data_split: list = [0.7, 0.2, 0.1],
+                 log_level: str = 'info', log_to_file: bool = True,
+                 num_processes: int = 1):
+    ''' create_model: ECRL's database/model creation workflow for all
+    publications
+
+    Args:
+        prop_abvr (str): abbreviation for the property name (e.g. CN)
+        smiles (list): if supplied with targets, creates a new database
+        targets (list): if supplied with smiles, creates a new database
+        db_name (str): you may supply an existing ECNet-formatted database
+        qspr_backend (str): if creating new database, generation software to
+            use (`padel`, `alvadesc`)
+        create_plots (bool): if True, creates plots for median absolute error
+            vs. number of descriptors as inputs, parity plot for all sets
+        data_split (list): [learn %, valid %, test %] for all supplied data
+        log_level (str): `debug`, `info`, `warn`, `error`, `crit`
+        log_to_file (bool): if True, saves workflow logs to a file in `logs`
+            directory
+        num_processes (int): number of concurrent processes to use for various
+            tasks
+    '''
+
+    # Initialize logging
+    logger.stream_level = log_level
+    if log_to_file:
+        logger.file_level = log_level
+
+    # If database not supplied, create database from supplied SMILES, targets
+    if db_name is None:
+        if smiles is None or targets is None:
+            raise ValueError('Must supply SMILES and target values')
+        db_name = datetime.now().strftime('{}_model_%Y%m%d.csv'.format(
+            prop_abvr
+        ))
+        logger.log('info', 'Creating database {}...'.format(db_name),
+                   'WORKFLOW')
+        create_db(smiles, db_name, targets, prop_abvr, backend=qspr_backend)
+        logger.log('info', 'Created database {}'.format(db_name), 'WORKFLOW')
+
+    # Create database split, each subset has proportionally equal number of
+    #   compounds based on range of experimental/target values
+    logger.log('info', 'Creating optimal data split...', 'WORKFLOW')
+    prop_range_from_split(db_name, data_split)
+    logger.log('info', 'Created optimal data split', 'WORKFLOW')
+    df = DataFrame(db_name)
+    df.create_sets()
+    logger.log('info', '\tLearning set: {}'.format(len(df.learn_set)),
+               'WORKFLOW')
+    logger.log('info', '\tValidation set: {}'.format(len(df.valid_set)),
+               'WORKFLOW')
+    logger.log('info', '\tTest set: {}'.format(len(df.test_set)), 'WORKFLOW')
+
+    # Find optimal number of QSPR input variables
+    logger.log('info', 'Finding optimal number of inputs...', 'WORKFLOW')
+    errors, desc = find_optimal_num_inputs(db_name, 'valid', num_processes)
+    df = DataFrame(db_name)
+    df.set_inputs(desc)
+    df.save(db_name.replace('.csv', '_opt.csv'))
+    logger.log('info', 'Found optimal number of inputs', 'WORKFLOW')
+    logger.log('info', '\tNumber of inputs: {}'.format(len(df._input_names)),
+               'WORKFLOW')
+
+    # Plot the curve of MAE vs. num. desc. added, if desired
+    if create_plots:
+        logger.log('info', 'Creating plot of MAE vs. descriptors...',
+                   'WORKFLOW')
+        num_add = [e[0] for e in errors]
+        maes = [e[1] for e in errors]
+        opt_num = len(desc)
+        plt.clf()
+        plt.rcParams['font.family'] = 'Times New Roman'
+        plt.plot(num_add, maes, c='blue')
+        plt.axvline(x=opt_num, c='red', linestyle='--')
+        plt.xlabel('Number of Descriptors as ANN Input Variables')
+        plt.ylabel('Median Absolute Error of {} Predictions'.format(prop_abvr))
+        plt.savefig(db_name.replace('.csv', '_desc_curve.png'))
+        logger.log('info', 'Created plot of MAE vs. descriptors', 'WORKFLOW')
+
+    # Tune ANN hyperparameters according to validation set performance
+    logger.log('info', 'Tuning ANN hyperparameters...', 'WORKFLOW')
+    config = default_config()
+    config = tune_hyperparameters(df, config, 25, 10, num_processes,
+                                  eval_set='valid', eval_fn='med_abs_error',
+                                  epochs=300, validate=False)
+    config['epochs'] = default_config()['epochs']
+    config_filename = db_name.replace('.csv', '.yml')
+    save_config(config, config_filename)
+    logger.log('info', 'Tuned ANN hyperparameters', 'WORKFLOW')
+    logger.log('info', '\tLearning rate: {}'.format(config['learning_rate']),
+               'WORKFLOW')
+    logger.log('info', '\tLR decay: {}'.format(config['decay']), 'WORKFLOW')
+    logger.log('info', '\tBatch size: {}'.format(config['batch_size']),
+               'WORKFLOW')
+    logger.log('info', '\tPatience: {}'.format(config['patience']), 'WORKFLOW')
+    logger.log('info', '\tHidden layers: {}'.format(config['hidden_layers']),
+               'WORKFLOW')
+
+    # Create Model
+    logger.log('info', 'Generating ANN...', 'WORKFLOW')
+    sv = Server(db_name.replace('.csv', '.yml'), num_processes=num_processes)
+    sv.load_data(db_name.replace('.csv', '_opt.csv'))
+    sv.create_project(db_name.replace('.csv', ''), 5, 25)
+    sv.train(validate=True, selection_set='valid',
+             selection_fn='med_abs_error')
+    logger.log('info', 'ANN Generated', 'WORKFLOW')
+    logger.log('info', 'Measuring ANN performance...', 'WORKFLOW')
+    preds_learn = sv.use(dset='learn')
+    preds_valid = sv.use(dset='valid')
+    preds_test = sv.use(dset='test')
+    learn_errors = sv.errors('r2', 'med_abs_error', dset='learn')
+    valid_errors = sv.errors('r2', 'med_abs_error', dset='valid')
+    test_errors = sv.errors('r2', 'med_abs_error', dset='test')
+    logger.log('info', 'Measured ANN performance', 'WORKFLOW')
+    logger.log('info', '\tLearning set:\t R2: {}\t MAE: {}'.format(
+        learn_errors['r2'], learn_errors['med_abs_error']), 'WORKFLOW')
+    logger.log('info', '\tValidation set:\t R2: {}\t MAE: {}'.format(
+        valid_errors['r2'], valid_errors['med_abs_error']), 'WORKFLOW')
+    logger.log('info', '\tTesting set:\t R2: {}\t MAE: {}'.format(
+        test_errors['r2'], test_errors['med_abs_error']), 'WORKFLOW')
+    sv.save_project(del_candidates=True)
+
+    if create_plots:
+        logger.log('info', 'Creating parity plot...', 'WORKFLOW')
+        plt.clf()
+        parity_plot = ParityPlot(
+            '',
+            'Experimental {} Value'.format(prop_abvr),
+            'Predicted {} Value'.format(prop_abvr)
+        )
+        parity_plot.add_series(sv._sets.learn_y, preds_learn, 'Learning Set',
+                               'blue')
+        parity_plot.add_series(sv._sets.valid_y, preds_valid, 'Validation Set',
+                               'green')
+        parity_plot.add_series(sv._sets.test_y, preds_test, 'Test Set', 'red')
+        parity_plot.add_error_bars(test_errors['med_abs_error'], 'Test MAE')
+        parity_plot._add_label('Test $R^2$', test_errors['r2'])
+        parity_plot._add_label('Validation MAE', valid_errors['med_abs_error'])
+        parity_plot._add_label('Validation $R^2$', valid_errors['r2'])
+        parity_plot._add_label('Learning MAE', learn_errors['med_abs_error'])
+        parity_plot._add_label('Learning $R^2$', learn_errors['r2'])
+        parity_plot.save(db_name.replace('.csv', '_parity.png'))
+        logger.log('info', 'Created parity plot', 'WORKFLOW')
diff --git a/ecnet/workflows/workflow_utils.py b/ecnet/workflows/workflow_utils.py
new file mode 100644
index 0000000..6333306
--- /dev/null
+++ b/ecnet/workflows/workflow_utils.py
@@ -0,0 +1,121 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+#
+# ecnet/workflows/workflow_utils.py
+# v.3.3.0
+# Developed in 2020 by Travis Kessler <travis.j.kessler@gmail.com>
+#
+# Functions used by the ECRL workflow
+#
+
+# Stdlib imports
+from multiprocessing import Pool, set_start_method
+from os import name
+
+# ECNet imports
+from ecnet.utils.data_utils import DataFrame
+from ecnet.utils.server_utils import default_config, train_model
+from ecnet.tasks.limit_inputs import limit_rforest
+
+
+def prop_range_from_split(db_name: str, data_split: list):
+    ''' prop_range_from_split: creates learning, validation, test subsets, each
+    with proportionally equal number of compounds based on experimental value
+    range
+
+    Args:
+        db_name (str): name/location of ECNet-formatted database
+        data_split (list): [learn %, valid %, test %] for all supplied data
+    '''
+
+    df = DataFrame(db_name)
+    for mol in df.data_points:
+        mol.assignment = 'L'
+        mol.TARGET = float(mol.TARGET)
+    df.data_points = sorted(df.data_points, key=lambda m: m.TARGET,
+                            reverse=True)
+    num_validate = int(len(df) / (data_split[1] * len(df.data_points)))
+    num_test = int(len(df) / (data_split[2] * len(df.data_points)))
+    for idx, mol in enumerate(df.data_points):
+        if (idx + 1) % num_validate == 0:
+            mol.assignment = 'V'
+        if (idx + 3) % num_test == 0:
+            mol.assignment = 'T'
+    if len([m for m in df.data_points if m.assignment == 'V']) == 0:
+        raise ValueError('Data split resulted in empty validation set')
+    if len([m for m in df.data_points if m.assignment == 'T']) == 0:
+        raise ValueError('Data split resulted in empty test set')
+    df.data_points = sorted(df.data_points, key=lambda m: m.id)
+    df.save(db_name)
+
+
+def find_optimal_num_inputs(db_name: str, eval_set: str,
+                            num_processes: int) -> tuple:
+    ''' find_optimal_num_inputs: find the optimal number of input variables,
+    return names of variables; optimal number of variables produces lowest
+    median absolute error; variables added 25 at a time, according to RFR
+    importance score (most-to-least important)
+
+    Args:
+        db_name (str): name/location of ECNet-formatted database
+        eval_set (str): set to evaluate (`learn`, `valid`, `train`, `test`,
+            None (all))
+        num_processes (int): number of concurrent processes to run for RFR,
+            training
+
+    Returns:
+        tuple: ([addition1, error1, ..., additionN, errorN], opt_desc)
+    '''
+
+    conf = default_config()
+    conf['epochs'] = 300
+    df = DataFrame(db_name)
+    df.create_sets()
+    conf['batch_size'] = len(df.learn_set)
+    desc = limit_rforest(df, len(df._input_names), num_processes=num_processes,
+                         eval_set=eval_set)
+    desc = [d[0] for d in desc]
+
+    errors = []
+    if num_processes > 1:
+        if name != 'nt':
+            set_start_method('spawn', force=True)
+        train_pool = Pool(processes=num_processes)
+
+    for d_idx in range(0, len(desc), 10):
+        if d_idx >= len(desc) - 1:
+            to_use = desc[:]
+        else:
+            to_use = desc[: d_idx + 1]
+        df = DataFrame(db_name)
+        df.set_inputs(to_use)
+        df.create_sets()
+        sets = df.package_sets()
+
+        if num_processes > 1:
+            errors.append([d_idx, train_pool.apply_async(
+                train_model, [
+                    sets, conf, eval_set, 'med_abs_error', False, '_.h5',
+                    False, False
+                ]
+            )])
+        else:
+            errors.append([d_idx, train_model(
+                sets, conf, eval_set, 'med_abs_error', False, '_.h5',
+                False, False
+            )[0]])
+
+    if num_processes > 1:
+        train_pool.close()
+        train_pool.join()
+        for idx, err in enumerate(errors):
+            errors[idx][1] = err[1].get()[0]
+
+    min_error = errors[0][1]
+    opt_num_desc = 1
+    for err in errors[1:]:
+        if err[1] < min_error:
+            min_error = err[1]
+            opt_num_desc = err[0]
+
+    return (errors, desc[: opt_num_desc])
diff --git a/setup.py b/setup.py
index 76539f4..540a1b9 100644
--- a/setup.py
+++ b/setup.py
@@ -1,28 +1,34 @@
 from setuptools import setup, find_packages
+from sys import argv
+
+install_requires = [
+    'alvadescpy==0.1.0',
+    'colorlogging==0.3.5',
+    'ecabc==2.2.3',
+    'matplotlib==3.1.2',
+    'numpy==1.16.4',
+    'padelpy==0.1.6',
+    'pyyaml==5.1.1',
+    'scikit-learn==0.21.2'
+]
+
+if '--omit_tf' in argv:
+    argv.remove('--omit_tf')
+else:
+    install_requires.append('tensorflow==2.0.0')
 
 setup(
     name='ecnet',
-    version='3.2.3',
+    version='3.3.0',
     description='UMass Lowell Energy and Combustion Research Laboratory Neural'
                 ' Network Software',
-    url='http://github.com/tjkessler/ecnet',
-    author='Travis Kessler, Hernan Gelaf-Romer, Sanskriti Sharma',
-    author_email='travis.j.kessler@gmail.com,'
-                 ' Hernan_Gelafromer@student.uml.edu,'
-                 ' Sanskriti_Sharma@student.uml.edu',
+    url='https://github.com/ecrl/ecnet',
+    author='Travis Kessler, John Hunter Mack',
+    author_email='Travis_Kessler@student.uml.edu,'
+                 ' Hunter_Mack@uml.edu',
     license='MIT',
     packages=find_packages(),
-    install_requires=[
-        'alvadescpy==0.1.0',
-        'colorlogging==0.3.5',
-        'ecabc==2.2.3',
-        'keras==2.2.4',
-        'matplotlib==3.1.0',
-        'numpy==1.16.4',
-        'padelpy==0.1.6',
-        'pyyaml==5.1.1',
-        'scikit-learn==0.21.2',
-        'tensorflow==1.13.1'
-    ],
+    python_requires='~=3.7',
+    install_requires=install_requires,
     zip_safe=False
 )
diff --git a/tests/models/test_mlp.py b/tests/models/test_mlp.py
index 51fd99f..f3420c1 100644
--- a/tests/models/test_mlp.py
+++ b/tests/models/test_mlp.py
@@ -15,7 +15,8 @@ def test_mlp(self):
         mlp.add_layer(9, 'relu', 3)
         mlp.add_layer(6, 'relu')
         mlp.add_layer(1, 'linear')
-        mlp.fit(array([[1, 1, 1], [0, 0, 0]]), array([[1], [0]]), epochs=20000)
+        mlp.fit(array([[1, 1, 1], [0, 0, 0]]), array([[1], [0]]), epochs=2000,
+                lr=0.01)
 
         results = mlp.use(array([[1, 1, 1], [0, 0, 0]]))
         self.assertAlmostEqual(results[0][0], 1, 3)
diff --git a/tests/tasks/test_limit_inputs.py b/tests/tasks/test_limit_inputs.py
index 1ef6862..dc4d319 100644
--- a/tests/tasks/test_limit_inputs.py
+++ b/tests/tasks/test_limit_inputs.py
@@ -23,6 +23,38 @@ def test_limit_rforest(self):
         df.set_inputs([r[0] for r in result])
         self.assertEqual(len(df._input_names), 2)
 
+    def test_limit_sets(self):
+
+        print('\nUNIT TEST: limit_sets')
+        df = DataFrame(DB_LOC)
+        df.create_sets()
+        result = limit_rforest(df, 2, eval_set='valid')
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 2)
+        df.set_inputs([r[0] for r in result])
+        self.assertEqual(len(df._input_names), 2)
+        df = DataFrame(DB_LOC)
+        df.create_sets()
+        result = limit_rforest(df, 2, eval_set='train')
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 2)
+        df.set_inputs([r[0] for r in result])
+        self.assertEqual(len(df._input_names), 2)
+        df = DataFrame(DB_LOC)
+        df.create_sets()
+        result = limit_rforest(df, 2, eval_set='test')
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 2)
+        df.set_inputs([r[0] for r in result])
+        self.assertEqual(len(df._input_names), 2)
+        df = DataFrame(DB_LOC)
+        df.create_sets()
+        result = limit_rforest(df, 2, eval_set=None)
+        self.assertEqual(len(result), 2)
+        self.assertEqual(len(result[0]), 2)
+        df.set_inputs([r[0] for r in result])
+        self.assertEqual(len(df._input_names), 2)
+
     def test_server_limit(self):
 
         print('\nUNIT TEST: limit_rforest (Server)')
diff --git a/tests/utils/test_server_utils.py b/tests/utils/test_server_utils.py
index 452c423..cbaa0fd 100644
--- a/tests/utils/test_server_utils.py
+++ b/tests/utils/test_server_utils.py
@@ -30,17 +30,18 @@ def test_default_config(self):
         self.assertEqual(
             dc, {
                 'epochs': 3000,
-                'learning_rate': 0.001,
+                'learning_rate': 0.01,
                 'beta_1': 0.9,
                 'beta_2': 0.999,
-                'epsilon': 0.0000001,
+                'epsilon': 1e-8,
                 'decay': 0.0,
                 'hidden_layers': [
                     [32, 'relu'],
                     [32, 'relu']
                 ],
                 'output_activation': 'linear',
-                'batch_size': 32
+                'batch_size': 32,
+                'patience': 128
             }
         )
 
@@ -126,17 +127,18 @@ def test_open_save_config(self):
             server_utils.open_config('config.yml'),
             {
                 'epochs': 3000,
-                'learning_rate': 0.001,
+                'learning_rate': 0.01,
                 'beta_1': 0.9,
                 'beta_2': 0.999,
-                'epsilon': 0.0000001,
+                'epsilon': 1e-8,
                 'decay': 0.0,
                 'hidden_layers': [
                     [32, 'relu'],
                     [32, 'relu']
                 ],
                 'output_activation': 'linear',
-                'batch_size': 32
+                'batch_size': 32,
+                'patience': 128
             }
         )
         remove('config.yml')
@@ -162,7 +164,7 @@ def test_train_model(self):
         pd = df.package_sets()
         config = server_utils.default_config()
         config['epochs'] = 100
-        r_squared = server_utils.train_model(
+        _ = server_utils.train_model(
             pd, config, 'test', 'r2', filename='test_train.h5'
         )
         self.assertTrue(exists('test_train.h5'))