-
Notifications
You must be signed in to change notification settings - Fork 45
/
standardize_confs.py
166 lines (129 loc) · 6.09 KB
/
standardize_confs.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
import glob, os, pickle, random, tqdm
from collections import defaultdict
from argparse import ArgumentParser
from scipy.optimize import linear_sum_assignment
from utils.standardization import *
RDLogger.DisableLog('rdApp.*')
parser = ArgumentParser()
parser.add_argument('--worker_id', type=int, required=True, help='Worker id to determine correct portion')
parser.add_argument('--out_dir', type=str, required=True, help='Output directory for the pickles')
parser.add_argument('--jobs_per_worker', type=int, default=1000, help='Number of molecules for each worker')
parser.add_argument('--root', type=str, default='data/DRUGS/drugs/', help='Directory with molecules pickle files')
parser.add_argument('--popsize', type=int, default=15, help='Population size for differential evolution')
parser.add_argument('--max_iter', type=int, default=15, help='Maximum number of iterations for differential evolution')
parser.add_argument('--confs_per_mol', type=int, default=30, help='Maximum number of conformers to take for each molecule')
parser.add_argument('--mmff', action='store_true', default=False, help='Whether to relax seed conformers with MMFF before matching')
parser.add_argument('--no_match', action='store_true', default=False, help='Whether to skip conformer matching')
parser.add_argument('--boltzmann', choices=['top', 'resample'], default=None, help='If set, specifies a different conformer selection policy')
args = parser.parse_args()
"""
Refers to the process of conformer matching to run before the start of training, takes the conformers from
a subset of the pickle files in the root directory and saves a final pickle for all of the. Example script:
for i in $(seq 0, 299); do
python standardize_confs.py --out_dir data/DRUGS/standardized_pickles --root data/DRUGS/drugs/ --confs_per_mol 30 --worker_id $i --jobs_per_worker 1000 &
done
"""
REMOVE_HS = lambda x: Chem.RemoveHs(x, sanitize=False)
def sort_confs(confs):
return sorted(confs, key=lambda conf: -conf['boltzmannweight'])
def resample_confs(confs, max_confs=None):
weights = [conf['boltzmannweight'] for conf in confs]
weights = np.array(weights) / sum(weights)
k = min(max_confs, len(confs)) if max_confs else len(confs)
return random.choices(confs, weights, k=k)
def log_error(err):
print(err)
long_term_log[err] += 1
return None
def conformer_match(name, confs):
long_term_log['confs_seen'] += len(confs)
if args.boltzmann == 'top':
confs = sort_confs(confs)
limit = args.confs_per_mol if args.boltzmann != 'resample' else None
confs = clean_confs(name, confs, limit=limit)
if not confs: return log_error("no_clean_confs")
if args.boltzmann == 'resample':
confs = resample_confs(confs, max_confs=args.confs_per_mol)
if args.confs_per_mol:
confs = confs[:args.confs_per_mol]
n_confs = len(confs)
new_confs = []
mol_rdkit = copy.deepcopy(confs[0]['rd_mol'])
rotable_bonds = get_torsion_angles(mol_rdkit)
if not rotable_bonds: return log_error("no_rotable_bonds")
mol_rdkit.RemoveAllConformers()
AllChem.EmbedMultipleConfs(mol_rdkit, numConfs=n_confs)
if mol_rdkit.GetNumConformers() != n_confs:
return log_error("rdkit_no_embed")
if args.mmff:
try:
mmff_func(mol_rdkit)
except:
return log_error("mmff_error")
if not args.no_match:
cost_matrix = [[get_von_mises_rms(confs[i]['rd_mol'], mol_rdkit, rotable_bonds, j) for j in range(n_confs)] for
i in range(n_confs)]
cost_matrix = np.asarray(cost_matrix)
row_ind, col_ind = linear_sum_assignment(cost_matrix)
else:
row_ind, col_ind = np.arange(len(confs)), np.arange(len(confs))
iterable = tqdm.tqdm(enumerate(confs), total=len(confs))
for i, conf in iterable:
mol = conf['rd_mol']
conf_id = int(col_ind[i])
try:
mol_rdkit_single = copy.deepcopy(mol_rdkit)
[mol_rdkit_single.RemoveConformer(j) for j in range(n_confs) if j != conf_id]
optimize_rotatable_bonds(mol_rdkit_single, mol, rotable_bonds,
popsize=args.popsize, maxiter=args.max_iter)
rmsd = AllChem.AlignMol(REMOVE_HS(mol_rdkit_single), REMOVE_HS(mol))
long_term_log['confs_success'] += 1
except Exception as e:
print(e)
long_term_log['confs_fail'] += 1
continue
conf['rd_mol'] = mol_rdkit_single
conf['rmsd'] = rmsd
conf['num_rotable_bonds'] = len(rotable_bonds)
new_confs.append(conf)
long_term_rmsd_cache.append(rmsd)
return new_confs
root = args.root
files = sorted(glob.glob(f'{root}*.pickle'))
files = files[args.worker_id * args.jobs_per_worker:(args.worker_id + 1) * args.jobs_per_worker]
master_dict = {}
print(len(files), 'jobs')
long_term_rmsd_cache = []
long_term_log = defaultdict(int)
for i, f in enumerate(files):
with open(f, "rb") as pkl:
mol_dic = pickle.load(pkl)
confs = mol_dic['conformers']
name = mol_dic["smiles"]
try:
new_confs = conformer_match(name, confs)
except Exception as e:
print(e)
long_term_log['mol_other_failure'] += 1
new_confs = None
if not new_confs:
print(f'{i} Failure nconfs={len(confs)} smi={name}')
else:
num_rotable_bonds = new_confs[0]['num_rotable_bonds']
rmsds = [conf['rmsd'] for conf in new_confs]
print(
f'{i} Success nconfs={len(new_confs)}/{len(confs)} bonds={num_rotable_bonds} rmsd={np.mean(rmsds):.2f} smi={name}')
mol_dic['conformers'] = new_confs
master_dict[f[len(root):-7]] = mol_dic
if (i + 1) % 20 == 0:
update = {
'mols_processed': i + 1,
'mols_success': len(master_dict),
'mean_rmsd': np.mean(long_term_rmsd_cache)
} | long_term_log
print(update)
print('ALL RMSD', np.mean(long_term_rmsd_cache))
if not os.path.isdir(args.out_dir):
os.mkdir(args.out_dir)
with open(args.out_dir + '/' + str(args.worker_id).zfill(3) + '.pickle', 'wb') as f:
pickle.dump(master_dict, f)