Pmapper is a Python module to generate 3D pharmacophore signatures and fingerprints. Signatures uniquely encode 3D pharmacophores with hashes suitable for fast identification of identical pharmacophores.
rdkit >= 2017.09
networkx >= 1.11
from rdkit import Chem
from rdkit.Chem import AllChem, ChemicalFeatures
from pharmacophore import Pharmacophore as P, read_smarts_feature_file, load_multi_conf_mol
from pprint import pprint# load a molecule from SMILES and generate 3D coordinates
mol = Chem.MolFromSmiles('C1CC(=O)NC(=O)C1N2C(=O)C3=CC=CC=C3C2=O') # talidomide
mol = Chem.AddHs(mol)
AllChem.EmbedMolecule(mol, randomSeed=42)
# load pharmacophore feature definitions from SMARTS file
smarts = read_smarts_feature_file('smarts_features.txt')
# create pharmacophore
p = P()
p.load_from_smarts(mol, smarts)
# get 3D pharmacophore signature
sig = p.get_signature_md5()
print(sig)Output:
f2e16f52f6f6ca6e97fc5844bfd35d36
sig = p.get_signature_md5(tol=5)
print(sig)Output:
fb535302db2e5d624aa979b6e8dfbdf2
# load pharmacophore using RDKit factory and get 3D pharmacophore signature
factory = ChemicalFeatures.BuildFeatureFactory('smarts_features.fdef')
p.load_from_feature_factory(mol, factory)
sig = p.get_signature_md5()
print(sig)Output:
f2e16f52f6f6ca6e97fc5844bfd35d36
# create multiple conformer molecule
AllChem.EmbedMultipleConfs(mol, numConfs=10, randomSeed=1024)
ps = load_multi_conf_mol(mol, smarts_features=smarts)
sig = [p.get_signature_md5() for p in ps]
pprint(sorted(sig)) # identical signatures occurOutput:
['13d168458ab1f251157f2422efcce312',
'13d168458ab1f251157f2422efcce312',
'182a4cfa756fe8b7f736a7f7ac0e8e0a',
'182a4cfa756fe8b7f736a7f7ac0e8e0a',
'4234e9d249874a5009f1e312dd885d80',
'ab273dd083c4f2e3424ba917b121b846',
'b6ec58553d2984bd398b4520bd1545cc',
'bfc43365b2657d08b6bb888e4d8ec71b',
'f5ca8e406dae31182e2b06fde7452b75',
'fc4a85e818fc0b3f034a7af42fa5ca69']
# generate 3D pharmacophore fingerprint which takes into account stereoconfiguration
b = p.get_fp(min_features=4, max_features=4) # set of activated bits
print(b)Output (a set of activated bit numbers):
{1922, 1795, 779, 1040, 528, 920, 154, 1437, 287, 1313, 1447, 1961, 941, 690, 1203, 65, 1346, 709, 1486, 1366, 2006, 1750, 1016, 346, 603, 1116, 354, 995, 228, 2024, 1900, 1524, 888, 2043}
Change settings:
b = p.get_fp(min_features=4, max_features=4, nbits=1024, activate_bits=2)
print(b)Output (a set of activated bit numbers):
{897, 514, 259, 389, 520, 264, 143, 16, 529, 656, 787, 660, 24, 285, 157, 32, 673, 550, 683, 173, 301, 558, 45, 945, 177, 692, 950, 443, 444, 61, 960, 961, 448, 321, 709, 197, 587, 460, 77, 718, 720, 80, 339, 596, 723, 470, 980, 345, 601, 476, 354, 614, 743, 1003, 875, 494, 367, 497, 114, 1012, 244, 630, 377, 762, 507, 508, 1021}
p.save_to_pma('filename.pma')Output is a text file having json format.
p = P()
p.load_from_pma('filename.pma')LigandScout models saved as pml-files can be read using p.load_ls_model. Also a pharmacophore can be stored in this format in order to export to LigandScout (p.save_ls_model).
Generation of pharmacopohre signatures (hashes) is a CPU-bound task. The computation speed depends on the number of features in pharmacophores.
Tests were run on 500 compounds (a random subset from Drugbank). Up to 50 conformers were generated for each compound. Up to 100 pharmacophores having a particular number of features were chosen randomly from the whole number of 25000 pharmacophores to generate pharmacophore signatures.
Laptop configuration:
- Intel(R) Core(TM) i7-5500U CPU @ 2.40GHz
- 12 GB RAM
- calculation was run in 1 thread (the module is thread safe and calculations can be parallelized)
pharmacophore generation: 19.21 s
total number of pharmacophores: 25000
pharmacophore hash generation:
50 pharmacophores having 2 features: 0.00 s; time per pharmacophore: 0.00000 s
100 pharmacophores having 3 features: 0.01 s; time per pharmacophore: 0.00010 s
100 pharmacophores having 4 features: 0.01 s; time per pharmacophore: 0.00010 s
100 pharmacophores having 5 features: 0.04 s; time per pharmacophore: 0.00040 s
100 pharmacophores having 6 features: 0.12 s; time per pharmacophore: 0.00120 s
100 pharmacophores having 7 features: 0.24 s; time per pharmacophore: 0.00240 s
100 pharmacophores having 8 features: 0.51 s; time per pharmacophore: 0.00510 s
100 pharmacophores having 9 features: 0.94 s; time per pharmacophore: 0.00940 s
100 pharmacophores having 10 features: 1.86 s; time per pharmacophore: 0.01860 s
100 pharmacophores having 11 features: 3.02 s; time per pharmacophore: 0.03020 s
100 pharmacophores having 12 features: 4.17 s; time per pharmacophore: 0.04170 s
100 pharmacophores having 13 features: 7.04 s; time per pharmacophore: 0.07040 s
100 pharmacophores having 14 features: 9.29 s; time per pharmacophore: 0.09290 s
100 pharmacophores having 15 features: 12.94 s; time per pharmacophore: 0.12940 s
100 pharmacophores having 16 features: 17.79 s; time per pharmacophore: 0.17790 s
100 pharmacophores having 17 features: 23.58 s; time per pharmacophore: 0.23580 s
100 pharmacophores having 18 features: 33.83 s; time per pharmacophore: 0.33830 s
100 pharmacophores having 19 features: 40.43 s; time per pharmacophore: 0.40430 s
100 pharmacophores having 20 features: 58.30 s; time per pharmacophore: 0.58300 s
Ligand-Based Pharmacophore Modeling Using Novel 3D Pharmacophore Signatures
Alina Kutlushina, Aigul Khakimova, Timur Madzhidov, Pavel Polishchuk
Molecules 2018, 23(12), 3094
https://doi.org/10.3390/molecules23123094
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