forked from aimclub/GOLEM
-
Notifications
You must be signed in to change notification settings - Fork 0
/
mol_encoders.py
212 lines (175 loc) · 10.5 KB
/
mol_encoders.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
import os
from typing import Any, List, Optional
import numpy as np
import torch
from gensim.models import word2vec, Word2Vec
from mol2vec.features import mol2alt_sentence, MolSentence
from rdkit.Chem import AllChem, RDKFingerprint, rdFingerprintGenerator
from rdkit.ML.Descriptors.MoleculeDescriptors import MolecularDescriptorCalculator
from examples.molecule_search.mol_adapter import MolAdapter
from examples.molecule_search.mol_transformer.transformer import create_masks, Transformer, EXTRA_CHARS, ALPHABET_SIZE
from examples.molecule_search.utils import download_from_github
from golem.core.log import default_log
from golem.core.paths import project_root
def adapter_func_to_molgraph(func):
""" Decorator function to adapt observation to MolGraphs graphs. """
def wrapper(obs):
mol_graph = MolAdapter().restore(obs)
embedding = func(mol_graph)
return embedding
return wrapper
def adapter_method_to_molgraph(func):
""" Decorator function to adapt observation to MolGraphs graphs. """
def wrapper(obj, obs):
mol_graph = MolAdapter().restore(obs)
embedding = func(obj, mol_graph)
return embedding
return wrapper
@adapter_func_to_molgraph
def ECFP(obs: Any):
""" Extended-Connectivity Fingerprint """
molecule = obs.get_rw_molecule()
feature_list = AllChem.GetMorganFingerprintAsBitVect(molecule,
radius=2,
nBits=2**10,
useFeatures=False,
useChirality=False)
return np.array(feature_list)
@adapter_func_to_molgraph
def RDKF(obs: Any):
""" RDK Fingerprint """
molecule = obs.get_rw_molecule()
fingerprint_rdk = RDKFingerprint(molecule)
return np.array(fingerprint_rdk)
@adapter_func_to_molgraph
def atom_pair(obs: Any):
""" Atom pair fingerprint """
molecule = obs.get_rw_molecule()
fingerprint = rdFingerprintGenerator.GetAtomPairGenerator(fpSize=1024).GetFingerprint(molecule)
return np.array(fingerprint)
@adapter_func_to_molgraph
def topological_torsion(obs: Any):
""" Topological Torsion fingerprint """
molecule = obs.get_rw_molecule()
fingerprint = rdFingerprintGenerator.GetTopologicalTorsionGenerator(fpSize=1024).GetFingerprint(molecule)
return np.array(fingerprint)
@adapter_func_to_molgraph
def mol_descriptors(obs: Any):
molecule = obs.get_rw_molecule()
chosen_descriptors = ['BalabanJ', 'BertzCT', 'Chi0', 'Chi0n', 'Chi0v', 'Chi1', 'Chi1n', 'Chi1v', 'Chi2n', 'Chi2v',
'Chi3n', 'Chi3v', 'Chi4n', 'Chi4v', 'EState_VSA1', 'EState_VSA10', 'EState_VSA11',
'EState_VSA2', 'EState_VSA3', 'EState_VSA4', 'EState_VSA5', 'EState_VSA6', 'EState_VSA7',
'EState_VSA8', 'EState_VSA9', 'ExactMolWt', 'FpDensityMorgan1', 'FpDensityMorgan2',
'FpDensityMorgan3', 'FractionCSP3', 'HallKierAlpha', 'HeavyAtomCount', 'HeavyAtomMolWt',
'Ipc', 'Kappa1', 'Kappa2', 'Kappa3', 'LabuteASA', 'MaxAbsEStateIndex', 'MaxAbsPartialCharge',
'MaxEStateIndex', 'MaxPartialCharge', 'MinAbsEStateIndex', 'MinAbsPartialCharge',
'MinEStateIndex', 'MinPartialCharge', 'MolLogP', 'MolMR', 'MolWt', 'NHOHCount', 'NOCount',
'NumAliphaticCarbocycles', 'NumAliphaticHeterocycles', 'NumAliphaticRings',
'NumAromaticCarbocycles', 'NumAromaticHeterocycles', 'NumAromaticRings', 'NumHAcceptors',
'NumHDonors', 'NumHeteroatoms', 'NumRadicalElectrons', 'NumRotatableBonds',
'NumSaturatedCarbocycles', 'NumSaturatedHeterocycles', 'NumSaturatedRings',
'NumValenceElectrons', 'PEOE_VSA1', 'PEOE_VSA10', 'PEOE_VSA11', 'PEOE_VSA12',
'PEOE_VSA13', 'PEOE_VSA14', 'PEOE_VSA2', 'PEOE_VSA3', 'PEOE_VSA4', 'PEOE_VSA5',
'PEOE_VSA6', 'PEOE_VSA7', 'PEOE_VSA8', 'PEOE_VSA9', 'RingCount', 'SMR_VSA1', 'SMR_VSA10',
'SMR_VSA2', 'SMR_VSA3', 'SMR_VSA4', 'SMR_VSA5', 'SMR_VSA6', 'SMR_VSA7', 'SMR_VSA8',
'SMR_VSA9', 'SlogP_VSA1', 'SlogP_VSA10', 'SlogP_VSA11', 'SlogP_VSA12', 'SlogP_VSA2',
'SlogP_VSA3', 'SlogP_VSA4', 'SlogP_VSA5', 'SlogP_VSA6', 'SlogP_VSA7', 'SlogP_VSA8',
'SlogP_VSA9', 'TPSA', 'VSA_EState1', 'VSA_EState10', 'VSA_EState2', 'VSA_EState3',
'VSA_EState4', 'VSA_EState5', 'VSA_EState6', 'VSA_EState7', 'VSA_EState8', 'VSA_EState9',
'fr_Al_COO', 'fr_Al_OH', 'fr_Al_OH_noTert', 'fr_ArN', 'fr_Ar_COO', 'fr_Ar_N', 'fr_Ar_NH',
'fr_Ar_OH', 'fr_COO', 'fr_COO2', 'fr_C_O', 'fr_C_O_noCOO', 'fr_C_S', 'fr_HOCCN', 'fr_Imine',
'fr_NH0', 'fr_NH1', 'fr_NH2', 'fr_N_O', 'fr_Ndealkylation1', 'fr_Ndealkylation2',
'fr_Nhpyrrole', 'fr_SH', 'fr_aldehyde', 'fr_alkyl_carbamate', 'fr_alkyl_halide',
'fr_allylic_oxid', 'fr_amide', 'fr_amidine', 'fr_aniline', 'fr_aryl_methyl', 'fr_azide',
'fr_azo', 'fr_barbitur', 'fr_benzene', 'fr_benzodiazepine', 'fr_bicyclic', 'fr_diazo',
'fr_dihydropyridine', 'fr_epoxide', 'fr_ester', 'fr_ether', 'fr_furan', 'fr_guanido',
'fr_halogen', 'fr_hdrzine', 'fr_hdrzone', 'fr_imidazole', 'fr_imide', 'fr_isocyan',
'fr_isothiocyan', 'fr_ketone', 'fr_ketone_Topliss', 'fr_lactam', 'fr_lactone', 'fr_methoxy',
'fr_morpholine', 'fr_nitrile', 'fr_nitro', 'fr_nitro_arom', 'fr_nitro_arom_nonortho',
'fr_nitroso', 'fr_oxazole', 'fr_oxime', 'fr_para_hydroxylation', 'fr_phenol',
'fr_phenol_noOrthoHbond', 'fr_phos_acid', 'fr_phos_ester', 'fr_piperdine', 'fr_piperzine',
'fr_priamide', 'fr_prisulfonamd', 'fr_pyridine', 'fr_quatN', 'fr_sulfide', 'fr_sulfonamd',
'fr_sulfone', 'fr_term_acetylene', 'fr_tetrazole', 'fr_thiazole', 'fr_thiocyan',
'fr_thiophene', 'fr_unbrch_alkane', 'fr_urea', 'qed']
mol_descriptor_calculator = MolecularDescriptorCalculator(chosen_descriptors)
list_of_descriptor_vals = list(mol_descriptor_calculator.CalcDescriptors(molecule))
return list_of_descriptor_vals
class Mol2Vec:
PRETRAINED_WORD2VEC = 'examples/molecule_search/data/pretrained_models/model_300dim.pkl'
GITHUB_URL = 'https://github.com/samoturk/mol2vec/raw/master/examples/models/model_300dim.pkl'
def __init__(self):
self.file_path = os.path.join(project_root(), Mol2Vec.PRETRAINED_WORD2VEC)
download_from_github(self.file_path,
Mol2Vec.GITHUB_URL,
message="Downloading pretrained model for molecules encoding...")
self.model = word2vec.Word2Vec.load(self.file_path)
@adapter_method_to_molgraph
def __call__(self, obs: Any):
molecule = obs.get_rw_molecule()
sentence = MolSentence(mol2alt_sentence(molecule, radius=1))
embedding = self.sentences2vec([sentence], self.model, unseen='UNK')[0]
return np.array(embedding).astype(float)
@staticmethod
def sentences2vec(sentences: List[MolSentence], model: Word2Vec, unseen: Optional[str] = None) -> np.array:
"""Generate vectors for each sentence (list) in a list of sentences. Vector is simply a
sum of vectors for individual words.
Parameters
----------
sentences : list, array
List with sentences
model : word2vec.Word2Vec
Gensim word2vec model
unseen : None, str
Keyword for unseen words. If None, those words are skipped.
https://stats.stackexchange.com/questions/163005/how-to-set-the-dictionary-for-text-analysis-using-neural-networks/163032#163032
Returns
-------
np.array
"""
keys = set(model.wv.key_to_index)
vec = []
if unseen:
unseen_vec = model.wv.get_vector(unseen)
for sentence in sentences:
if unseen:
vec.append(sum([model.wv.get_vector(y) if y in set(sentence) & keys
else unseen_vec for y in sentence]))
else:
vec.append(sum([model.wv.get_vector(y) for y in sentence
if y in set(sentence) & keys]))
return np.array(vec)
class MoleculeTransformer:
""" Based on https://github.com/mpcrlab/MolecularTransformerEmbeddings """
PRETRAINED_TRANSFORMER = 'examples/molecule_search/data/pretrained_models/pretrained.ckpt'
GITHUB_URL = 'https://github.com/mpcrlab/MolecularTransformerEmbeddings/releases/download/' \
'checkpoints/pretrained.ckpt'
def __init__(self, embedding_size: int = 512, num_layers: int = 6, max_length: int = 256):
self.log = default_log(self)
self.file_path = os.path.join(project_root(), MoleculeTransformer.PRETRAINED_TRANSFORMER)
download_from_github(self.file_path,
MoleculeTransformer.GITHUB_URL,
message="Downloading pretrained model for molecules encoding...")
self.model = self._model_setup(embedding_size, num_layers)
self.encoder = self.model.encoder.cpu()
self.max_length = max_length
def _model_setup(self, embedding_size: int, num_layers: int):
model = Transformer(ALPHABET_SIZE, embedding_size, num_layers).eval()
model = torch.nn.DataParallel(model)
checkpoint = torch.load(self.file_path, map_location=torch.device("cpu"))
model.load_state_dict(checkpoint['state_dict'])
return model.module.cpu()
@adapter_method_to_molgraph
def __call__(self, obs: Any):
smiles = obs.get_smiles()
with torch.no_grad():
encoded = self.encode_smiles(smiles)
mask = create_masks(encoded)
embedding = self.encoder(encoded, mask)[0].numpy()
embedding = embedding.mean(axis=0)
return embedding
@staticmethod
def encode_char(c):
return ord(c) - 32
def encode_smiles(self, string: str, start_char=EXTRA_CHARS['seq_start']):
return torch.tensor([ord(start_char)] +
[self.encode_char(c) for c in string], dtype=torch.long)[:self.max_length].unsqueeze(0)