CReM is an open-source Python framework to generate chemical structures using a fragment-based approach.
The main idea behind is similar to matched molecular pairs considering context that fragments in the identical context are interchangeable. Therefore, one can create a database of interchangeable fragments and use it for generation of chemically valid structures.
Features:
- Generation of a custom fragment database
- Three modes of structure generation: MUTATE, GROW, LINK
- Context radius to consider for replacement
- Fragment size to replace and the size of a replacing fragment
- Protection of atoms from modification (e.g. scaffold protection)
- Replacements with fragments occurred in a fragment database with certain minimal frequency
- Make randomly chosen replacements up to the specified number
Limitations and known issues
- New ring systems cannot be constructed from fragments, thus representativeness of ring systems in generated structures depends on a used fragment database. We are working on that issue.
- Very large molecules will not be processed by CReM. If a molecule has more than 30 non-ring single bonds it will not be MUTATED. If a molecule has more than 100 hydrogen atoms it will not be processed by GROW and LINK.
- Canonicalisation of contexts depends on RDKit SMILES representation. Thus, changing in RDKit SMILES representation may affect fragment databases and make impossible to use a database prepared with previous RDKit version from code running under later RDKit versions.
https://crem.readthedocs.io/en/latest/
To play with a tool online.
https://crem.imtm.cz/
Several command line utilities will be installed to create fragment databases and crem module will become available in Python imports to generate structures.
From pypi package
pip install crem
Manually from repository
git clone https://github.com/DrrDom/crem
cd crem
python3 setup.py sdist bdist_wheel
pip install dist/crem-0.1-py3-none-any.whl
Uninstall
pip uninstall crem
crem requires rdkit>=2017.09. To run the guacamol test guacamol should be installed.
This step is required if you want to generate a custom fragment database. You can download precompiled databases obtained by fragmentation of the whole ChEMBL by links provided on this page - http://www.qsar4u.com/pages/crem.php.
For convenience there is the bash script crem_create_frag_db.sh which includes all steps below. It takes three positional arguments: input file with SMILES, output directory where intermediate files and a final database will be stored and number of CPUs to use (this is optional, default value is 1).
crem_create_frag_db.sh input.smi fragdb_dir 32
Fragmentation of input structures:
fragmentation -i input.smi -o frags.txt -c 32 -v
Convert fragments to standardized representation of a core and a context of a given radius:
frag_to_env -i frags.txt -o r3.txt -r 3 -c 32 -v
Remove duplicated lines in the output file and count frequency of occurrence of fragemnt-context pairs. These (sort and uniq) are bash utilities but since Win10 is Linux-friendly that should not be a big issue for Win users to execute them
sort r3.txt | uniq -c > r3_c.txt
Create DB and import the file to a database table
env_to_db -i r3_c.txt -o fragments.db -r 3 -c -v
Last three steps should be executed for each radius. All tables can be stored in the same database.
Import necessary functions from the main module
from crem.crem import mutate_mol, grow_mol, link_mols
from rdkit import ChemCreate a molecute and mutate it. Only one heavy atom will be substituted. Default radius is 3.
m = Chem.MolFromSmiles('c1cc(OC)ccc1C') # methoxytoluene
mols = list(mutate_mol(m, db_name='replacements.db', max_size=1))output example
['CCc1ccc(C)cc1',
'CC#Cc1ccc(C)cc1',
'C=C(C)c1ccc(C)cc1',
'CCCc1ccc(C)cc1',
'CC=Cc1ccc(C)cc1',
'CCCCc1ccc(C)cc1',
'CCCOc1ccc(C)cc1',
'CNCCc1ccc(C)cc1',
'COCCc1ccc(C)cc1',
...
'Cc1ccc(C(C)(C)C)cc1']
Add hydrogens to the molecule to mutate hydrogens as well
mols = list(mutate_mol(Chem.AddHs(m), db_name='replacements.db', max_size=1))output
['CCc1ccc(C)cc1',
'CC#Cc1ccc(C)cc1',
'C=C(C)c1ccc(C)cc1',
'CCCc1ccc(C)cc1',
'Cc1ccc(C(C)C)cc1',
'CC=Cc1ccc(C)cc1',
...
'COc1ccc(C)cc1C',
'C=Cc1cc(C)ccc1OC',
'COc1ccc(C)cc1Cl',
'COc1ccc(C)cc1CCl']
Grow molecule. Only hydrogens will be replaced. Hydrogens should not be added explicitly.
mols = list(grow_mol(m, db_name='replacements_sc2.db'))output
['COc1ccc(C)c(Br)c1',
'COc1ccc(C)c(C)c1',
'COc1ccc(C)c(Cl)c1',
'COc1ccc(C)c(OC)c1',
'COc1ccc(C)c(N)c1',
...
'COc1ccc(CCN)cc1']
Create the second molecule and link it to toluene
m2 = Chem.MolFromSmiles('NCC(=O)O') # glycine
mols = list(link_mols(m, m2, db_name='replacements.db'))output
['Cc1ccc(OCC(=O)NCC(=O)O)cc1',
'Cc1ccc(OCCOC(=O)CN)cc1',
'COc1ccc(CC(=N)NCC(=O)O)cc1',
'COc1ccc(CC(=O)NCC(=O)O)cc1',
'COc1ccc(CC(=S)NCC(=O)O)cc1',
'COc1ccc(CCOC(=O)CN)cc1']
You can vary the size of a linker and specify the distance between two attachment points in a linking fragment. There are many other arguments available in these functions, look at their docstrings for details.
All functions have an argument ncores and can make mupltile replacement in one molecule in parallel. If you want to process several molecules in parallel you have to write your own code. However, the described functions are generators and cannot be used with multiprocessing module. Therefore, three complementary functions mutate_mol2, grow_mol2 and link_mols2 were created. They return the list with results and can be pickled and used with multiprocessing.Pool or other tools.
Example:
from multiprocessing import Pool
from functools import partial
from crem.crem import mutate_mol2
from rdkit import Chem
p = Pool(2)
input_smi = ['c1ccccc1N', 'NCC(=O)OC', 'NCCCO']
input_mols = [Chem.MolFromSmiles(s) for s in input_smi]
res = list(p.imap(partial(mutate_mol2, db_name='replacements.db', max_size=1), input_mols))res would be a list of lists with SMILES of generated molecules
| task | SMILES LSTM* | SMILES GA* | Graph GA* | Graph MCTS* | CReM |
|---|---|---|---|---|---|
| Celecoxib rediscovery | 1.000 | 0.732 | 1.000 | 0.355 | 1.000 |
| Troglitazone rediscovery | 1.000 | 0.515 | 1.000 | 0.311 | 1.000 |
| Thiothixene rediscovery | 1.000 | 0.598 | 1.000 | 0.311 | 1.000 |
| Aripiprazole similarity | 1.000 | 0.834 | 1.000 | 0.380 | 1.000 |
| Albuterol similarity | 1.000 | 0.907 | 1.000 | 0.749 | 1.000 |
| Mestranol similarity | 1.000 | 0.79 | 1.000 | 0.402 | 1.000 |
| C11H24 | 0.993 | 0.829 | 0.971 | 0.410 | 0.966 |
| C9H10N2O2PF2Cl | 0.879 | 0.889 | 0.982 | 0.631 | 0.940 |
| Median molecules 1 | 0.438 | 0.334 | 0.406 | 0.225 | 0.371 |
| Median molecules 2 | 0.422 | 0.38 | 0.432 | 0.170 | 0.434 |
| Osimertinib MPO | 0.907 | 0.886 | 0.953 | 0.784 | 0.995 |
| Fexofenadine MPO | 0.959 | 0.931 | 0.998 | 0.695 | 1.000 |
| Ranolazine MPO | 0.855 | 0.881 | 0.92 | 0.616 | 0.969 |
| Perindopril MPO | 0.808 | 0.661 | 0.792 | 0.385 | 0.815 |
| Amlodipine MPO | 0.894 | 0.722 | 0.894 | 0.533 | 0.902 |
| Sitagliptin MPO | 0.545 | 0.689 | 0.891 | 0.458 | 0.763 |
| Zaleplon MPO | 0.669 | 0.413 | 0.754 | 0.488 | 0.770 |
| Valsartan SMARTS | 0.978 | 0.552 | 0.990 | 0.04 | 0.994 |
| Deco Hop | 0.996 | 0.970 | 1.000 | 0.590 | 1.000 |
| Scaffold Hop | 0.998 | 0.885 | 1.000 | 0.478 | 1.000 |
| total score | 17.341 | 14.398 | 17.983 | 9.011 | 17.919 |
BSD-3
CReM: chemically reasonable mutations framework for structure generation
Pavel Polishchuk
Journal of Cheminformatics 2020, 12, (1), 28
https://doi.org/10.1186/s13321-020-00431-w
Control of Synthetic Feasibility of Compounds Generated with CReM
Pavel Polishchuk
Journal of Chemical Information and Modeling 2020, 60, 6074-6080
https://dx.doi.org/10.1021/acs.jcim.0c00792