Cluster-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw, AllChem\n",
    "from rdkit.Chem.Scaffolds import MurckoScaffold\n",
    "from rdkit.Chem import Descriptors\n",
    "import pandas as pd\n",
    "import numpy\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "import random\n",
    "from random import choices\n",
    "from rdkit import DataStructs\n",
    "from rdkit.DataStructs import cDataStructs\n",
    "from rdkit.ML.Cluster import Butina\n",
    "from rdkit.Chem import rdFingerprintGenerator\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import rdFMCS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "suppl = Chem.SDMolSupplier('../Phase-CQS/Common_Comp_set_topo.sdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def readMOl(A):\n",
    "    data = []\n",
    "    Mols = []\n",
    "    for mol in suppl:\n",
    "        if mol is not None:\n",
    "            R = Chem.MolToSmiles(mol)\n",
    "            if R not in data:\n",
    "                data.append(R)\n",
    "                Mols.append(mol)\n",
    "\n",
    "    return(Mols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x7f3c990691c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "moles = readMOl(suppl)\n",
    "moles[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of compounds converted: 318\n",
      "Fingerprint length per compound: 2048\n"
     ]
    }
   ],
   "source": [
    "# Create fingerprints for all molecules\n",
    "rdkit_gen = rdFingerprintGenerator.GetRDKitFPGenerator(maxPath=5)\n",
    "fingerprints = [rdkit_gen.GetFingerprint(m) for m in moles]\n",
    "\n",
    "# How many compounds/fingerprints do we have?\n",
    "print('Number of compounds converted:',len(fingerprints))\n",
    "print('Fingerprint length per compound:',len(fingerprints[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate distance matrix for fingerprint list\n",
    "def Tanimoto_distance_matrix(fp_list):\n",
    "    dissimilarity_matrix = []\n",
    "    for i in range(1,len(fp_list)):\n",
    "        similarities = DataStructs.BulkTanimotoSimilarity(fp_list[i],fp_list[:i])\n",
    "        # Since we need a distance matrix, calculate 1-x for every element in similarity matrix\n",
    "        dissimilarity_matrix.extend([1-x for x in similarities])\n",
    "    return dissimilarity_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Input: Fingerprints and a threshold for the clustering\n",
    "def ClusterFps(fps,cutoff=0.2):\n",
    "    # Calculate Tanimoto distance matrix\n",
    "    distance_matr = Tanimoto_distance_matrix(fps)\n",
    "    # Now cluster the data with the implemented Butina algorithm:\n",
    "    clusters = Butina.ClusterData(distance_matr,len(fps),cutoff,isDistData=True)\n",
    "    return clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total # clusters:  66\n",
      "# clusters with only 1 compound:  33\n",
      "# clusters with >5 compounds:  10\n",
      "# clusters with >25 compounds:  3\n",
      "# clusters with >100 compounds:  0\n"
     ]
    }
   ],
   "source": [
    "# Run the clustering procedure for the dataset\n",
    "clusters = ClusterFps(fingerprints,cutoff=0.2)\n",
    "\n",
    "# Give a short report about the numbers of clusters and their sizes\n",
    "num_clust_g1 = len([c for c in clusters if len(c) == 1])\n",
    "num_clust_g5 = len([c for c in clusters if len(c) > 5])\n",
    "num_clust_g25 = len([c for c in clusters if len(c) > 25])\n",
    "num_clust_g100 = len([c for c in clusters if len(c) > 100])\n",
    "\n",
    "print(\"total # clusters: \", len(clusters))\n",
    "print(\"# clusters with only 1 compound: \", num_clust_g1)\n",
    "print(\"# clusters with >5 compounds: \", num_clust_g5)\n",
    "print(\"# clusters with >25 compounds: \", num_clust_g25)\n",
    "print(\"# clusters with >100 compounds: \", num_clust_g100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of clusters 66 from 318 molecules at distance cut-off 0.20\n",
      "Number of molecules in largest cluster: 65\n",
      "Similarity between two random points in same cluster 0.86\n",
      "Similarity between two random points in different cluster 0.22\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dist_co = 0.2\n",
    "clusters = ClusterFps(fingerprints,cutoff=dist_co)\n",
    "\n",
    "# Plot the size of the clusters - save plot\n",
    "fig = plt.figure(1, figsize=(8, 2.5))\n",
    "plt1 = plt.subplot(111)\n",
    "plt.axis([0, len(clusters), 0, len(clusters[0])+1])\n",
    "plt.xlabel('Cluster index', fontsize=20)\n",
    "plt.ylabel('# molecules', fontsize=20)\n",
    "plt.tick_params(labelsize=16)\n",
    "plt1.bar(range(1, len(clusters)), [len(c) for c in clusters[:len(clusters)-1]], lw=0)\n",
    "plt.title('Threshold: '+str('%3.1f' %dist_co), fontsize=20)\n",
    "plt.savefig(\"cluster_dist_cutoff_%4.2f.png\" %dist_co, dpi=300, bbox_inches=\"tight\", transparent=True)\n",
    "\n",
    "print('Number of clusters %d from %d molecules at distance cut-off %4.2f' %(len(clusters), len(moles), dist_co))\n",
    "print('Number of molecules in largest cluster:', len(clusters[0]))\n",
    "print('Similarity between two random points in same cluster %4.2f'%DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]],fingerprints[clusters[0][1]]))\n",
    "print('Similarity between two random points in different cluster %4.2f'%DataStructs.TanimotoSimilarity(fingerprints[clusters[0][0]],fingerprints[clusters[1][0]]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Write cluster members in file\n",
    "def clusterMember(Number):\n",
    "    filename = '../Phase-CQS/cluster/cluster-'+str(Number)+'.sdf'\n",
    "    w = Chem.SDWriter(filename)\n",
    "    dd = list(clusters[Number])\n",
    "    print(dd)\n",
    "    for x in dd:\n",
    "        Name = moles[x].GetProp('_Name')\n",
    "        print(Name)\n",
    "        w.write(moles[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[248, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[119, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 120, 121, 122, 123, 124, 125, 126, 127, 128, 131, 132, 133, 134, 141, 183, 189]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[14, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 29, 30, 196, 197, 198]\n",
      "CHEMBL3740844\n",
      "CHEMBL3739689\n",
      "CHEMBL3740472\n",
      "CHEMBL3740669\n",
      "CHEMBL3741918\n",
      "CHEMBL3741377\n",
      "CHEMBL3739611\n",
      "CHEMBL3741608\n",
      "CHEMBL3742145\n",
      "CHEMBL3741062\n",
      "CHEMBL3741595\n",
      "CHEMBL3740732\n",
      "CHEMBL3741224\n",
      "CHEMBL3739931\n",
      "CHEMBL3740612\n",
      "CHEMBL3739647\n",
      "CHEMBL3741787\n",
      "CHEMBL3741821\n",
      "CHEMBL3739862\n",
      "CHEMBL3741733\n",
      "CHEMBL3740864\n",
      "CHEMBL3740278\n",
      "CHEMBL3740030\n",
      "CHEMBL3739459\n",
      "CHEMBL3740400\n",
      "CHEMBL3739470\n",
      "CHEMBL3739826\n",
      "CHEMBL3741548\n",
      "\n",
      "\n",
      "\n",
      "[182, 142, 154, 155, 156, 157, 159, 160, 161, 162, 163, 165, 167, 168, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[129]\n",
      "\n",
      "[297, 84, 278, 279, 281, 283, 285, 291, 292, 293, 295, 296, 299, 300, 301, 302, 303, 304]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[78, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77]\n",
      "Compound125\n",
      "Compound190\n",
      "Compound191\n",
      "Compound196\n",
      "Compound197\n",
      "Compound199\n",
      "Compound209\n",
      "Compound210\n",
      "Compound224\n",
      "Compound227\n",
      "Compound236\n",
      "Compound241\n",
      "Compound246\n",
      "Compound251\n",
      "Compound53\n",
      "Compound122\n",
      "[27, 28]\n",
      "CHEMBL3741322\n",
      "CHEMBL3742080\n",
      "[312, 280, 282, 284, 286, 287, 306, 307, 309, 310, 311]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[59, 37, 38, 43, 44, 45, 47, 48, 56]\n",
      "ZINC000010334464\n",
      "ZINC000010331692\n",
      "ZINC000010337247\n",
      "ZINC000222523897\n",
      "ZINC000222537290\n",
      "ZINC000220387986\n",
      "ZINC000220029015\n",
      "ZINC000220389650\n",
      "ZINC000220389972\n",
      "[181, 130, 137, 138, 145, 146, 147, 148]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[271, 265, 266, 267, 268, 269, 270]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[276, 272, 273, 274, 275]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[169, 153]\n",
      "\n",
      "\n",
      "[96, 93, 94, 95]\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "[195, 193, 194]\n",
      "\n",
      "\n",
      "\n",
      "[192, 190, 191]\n",
      "\n",
      "\n",
      "\n",
      "[186, 184, 185]\n",
      "\n",
      "\n",
      "\n",
      "[166, 158, 164]\n",
      "\n",
      "\n",
      "\n",
      "[152, 144, 151]\n",
      "\n",
      "\n",
      "\n",
      "[85, 86, 87]\n",
      "\n",
      "\n",
      "\n",
      "[58, 36, 53]\n",
      "ZINC000020799181\n",
      "ZINC000020799087\n",
      "ZINC000020799083\n",
      "[54, 35, 46]\n",
      "ZINC000006653925\n",
      "ZINC000408628005\n",
      "ZINC000018835732\n",
      "[51, 41, 50]\n",
      "MolPort-003-907-630\n",
      "ZINC000004583733\n",
      "ZINC000004583730\n",
      "[315]\n",
      "\n",
      "[314, 313]\n",
      "\n",
      "\n",
      "[298, 294]\n",
      "\n",
      "\n",
      "[290, 289]\n",
      "\n",
      "\n",
      "[143, 140]\n",
      "\n",
      "\n",
      "[136, 135]\n",
      "\n",
      "\n",
      "[92, 90]\n",
      "\n",
      "\n",
      "[80, 79]\n",
      "Compound94\n",
      "Compound229\n",
      "[61, 60]\n",
      "MolPort-002-947-060\n",
      "MolPort-002-987-397\n",
      "[40, 39]\n",
      "ZINC000952975500\n",
      "ZINC000952975500\n",
      "[32, 31]\n",
      "CHEMBL123632\n",
      "CHEMBL8602\n",
      "[317]\n",
      "\n",
      "[316]\n",
      "\n",
      "[308]\n",
      "\n",
      "[305]\n",
      "\n",
      "[288]\n",
      "\n",
      "[277]\n",
      "\n",
      "[264]\n",
      "\n",
      "[188]\n",
      "\n",
      "[187]\n",
      "\n",
      "[150]\n",
      "\n",
      "[149]\n",
      "\n",
      "[139]\n",
      "\n",
      "[100]\n",
      "\n",
      "[99]\n",
      "\n",
      "[98]\n",
      "\n",
      "[97]\n",
      "\n",
      "[91]\n",
      "\n",
      "[89]\n",
      "\n",
      "[88]\n",
      "\n",
      "[83]\n",
      "\n",
      "[82]\n",
      "2-[1-[(2-chloro-4-methoxy-phenyl)methyl]-4-methyl-pyrrolidin-3-yl]-7-tetrahydropyran-4-yl-3H-imidazo[5,1-f][1,2,4]triazin-4-one\n",
      "[81]\n",
      "Compound204\n",
      "[62]\n",
      "MFCD04109603\n",
      "[57]\n",
      "MolPort-020-232-218\n",
      "[55]\n",
      "ZINC000014729887\n",
      "[52]\n",
      "ZINC000014730375\n",
      "[49]\n",
      "ZINC000244633343\n",
      "[42]\n",
      "ZINC000013572017\n",
      "[34]\n",
      "CHEMBL2158972\n",
      "[33]\n",
      "CHEMBL1778269\n"
     ]
    }
   ],
   "source": [
    "for s in range(0,65):\n",
    "    clusterMember(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def clusterMCS(Number):\n",
    "    ml = []\n",
    "    for x in clusters[Number]:\n",
    "        ml.append(moles[x])\n",
    "    \n",
    "    \n",
    "    return(ml)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SMARTS string: [#6]1(:[#7]:[#6]:[#7](:[#6]:1)-[#6])-[#6](-[#7]-[#6]1-[#6](-[#7](-[#6]-[#6]2:[#6]:[#6]:[#6]:[#6]:[#6]:2)-[#6])-[#6](-[#8])-[#6](-[#6]-1)-[#8]-[#6]1:[#6]:[#6]:[#6]:[#6]:[#6]:1)=[#8]\n",
      "SMARTS string: CN(CC1:C:C:C:C:C:1)C1C(NC(=O)C2:C:N(C):C:N:2)CC(OC2:C:C:C:C:C:2)C1O\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=300x300 at 0x7F3C98A27B00>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "jj = clusterMCS(30)\n",
    "mcs1 = rdFMCS.FindMCS(jj)\n",
    "print('SMARTS string: %s'%mcs1.smartsString)\n",
    "# Draw substructure\n",
    "m1 = Chem.MolFromSmarts(mcs1.smartsString)\n",
    "m2 = Chem.MolToSmiles(m1)\n",
    "print('SMARTS string: %s'%m2)\n",
    "Draw.MolToImage(m1,subImgSize=(400,400))\n",
    "\n",
    "#img.save('../../Phase/cluster/Cluster-MCS-5.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ten molecules from largest cluster:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1000x400 at 0x7F44C683CB38>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print ('Ten molecules from largest cluster:')\n",
    "# Draw molecules\n",
    "\n",
    "Draw.MolsToGridImage([Chem.MolFromSmiles(Chem.MolToSmiles(moles[i])) for i in clusters[0][:10]], legends=[moles[i].GetProp('_Name') for i in clusters[0][:10]], molsPerRow=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ten molecules from first 10 clusters:\n"
     ]
    }
   ],
   "source": [
    "print ('Ten molecules from first 10 clusters:')\n",
    "# Draw molecules\n",
    "img = Draw.MolsToGridImage([Chem.MolFromSmiles(Chem.MolToSmiles(moles[clusters[i][0]])) for i in range(60,66)], \n",
    "                     legends=[moles[clusters[i][0]].GetProp('_Name') for i in range(60,66)], \n",
    "                     molsPerRow=5,subImgSize=(250,250))\n",
    "\n",
    "img.save('../Phase-CQS/cluster/cluster-60-66-Rep.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MOlToSmile(A):\n",
    "    data = []\n",
    "    scaffold = []\n",
    "    for mol in A:\n",
    "        if mol is not None:\n",
    "            name = mol.GetProp('_Name')\n",
    "            core=MurckoScaffold.MakeScaffoldGeneric(mol)\n",
    "            S = Chem.MolToSmiles(core)\n",
    "            if S not in data:\n",
    "                data.append(S)\n",
    "                scaffold.append(core)\n",
    "\n",
    "    return(S)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:cadd-py36]",
   "language": "python",
   "name": "conda-env-cadd-py36-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}