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mol_metrics.py
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mol_metrics.py
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import gzip
import math
import pickle
import numpy as np
import pandas as pd
from rdkit import Chem
from rdkit import rdBase
from rdkit.Chem import QED
from rdkit import DataStructs
from rdkit.Chem import PandasTools, Crippen
from rdkit.Chem.rdMolDescriptors import GetMorganFingerprintAsBitVect
rdBase.DisableLog('rdApp.error')
# ===========================
class Tokenizer():
def __init__(self):
self.pad = '_'
self.seq_start = '<'
self.seq_end = '>'
self.mask_start = '{'
self.mask_end = '}'
self.mask = '*'
# Define every token of the vocabulary
def build_vocab(self):
chars=[]
# atoms (carbon), replace Cl for Q and Br for W
chars = chars + ['H', 'B', 'c', 'C', 'n', 'N', 'o', 'O', 'p', 'P', 's', 'S', 'F', 'Q', 'W', 'I']
# hidrogens: H2 to Z, H3 to X
chars = chars + ['[', ']', '+', 'Z', 'X']
# bounding
chars = chars + ['-', '=', '#', '.']
# branches
chars = chars + ['(', ')']
# cycles
chars = chars + ['1', '2', '3', '4', '5', '6', '7']
# anit/clockwise
chars = chars + ['@']
# directional bonds
chars = chars + ['/', '\\']
#Important that pad gets value 0
self.tokenlist = [self.pad, self.seq_start, self.seq_end, self.mask_start, self.mask_end, self.mask] + list(chars)
@property
def tokenlist(self):
return self._tokenlist
@tokenlist.setter
def tokenlist(self, tokenlist):
self._tokenlist = tokenlist
# create the dictionaries
self.char_to_int = {c:i for i,c in enumerate(self._tokenlist)}
self.int_to_char = {i:c for c,i in self.char_to_int.items()}
# Encode a scaffold to a numerical list with seq_start and seq_end tokens
def scaffold_encode(self, smi):
encoded = []
smi = smi.replace('Cl', 'Q')
smi = smi.replace('Br', 'W')
# hydrogens
smi = smi.replace('H2', 'Z')
smi = smi.replace('H3', 'X')
return [self.char_to_int[self.seq_start]] + [self.char_to_int[s] for s in smi] + [self.char_to_int[self.seq_end]]
# Encode a decoration to a numerical list with mask_start and mask_end tokens
def decoration_encode(self, smi):
encoded = []
smi = smi.replace('Cl', 'Q')
smi = smi.replace('Br', 'W')
# hydrogens
smi = smi.replace('H2', 'Z')
smi = smi.replace('H3', 'X')
return [self.char_to_int[self.mask_start]] + [self.char_to_int[s] for s in smi] + [self.char_to_int[self.mask_end]]
# Decode the numerical list to a SMILES string
def decode(self, ords):
smi = ''.join([self.int_to_char[o] for o in ords])
# hydrogens
smi = smi.replace('Z', 'H2')
smi = smi.replace('X', 'H3')
# replace proxy atoms for double char atoms symbols
smi = smi.replace('Q', 'Cl')
smi = smi.replace('W', 'Br')
return smi
# Define the vocabulary size
@property
def n_tokens(self):
return len(self.int_to_char)
# ===========================
# Select chemical properties
def reward_fn(properties, generated_smiles):
if properties == 'druglikeness':
vals = batch_druglikeness(generated_smiles)
elif properties == 'solubility':
vals = batch_solubility(generated_smiles)
elif properties == 'synthesizability':
vals = batch_SA(generated_smiles)
elif properties == 'DRD2':
vals = batch_DRD2(generated_smiles)
return vals
# ===========================
# Druglikeness
def batch_druglikeness(smiles):
vals = []
for sm in smiles:
if len(sm) != 0:
mol = Chem.MolFromSmiles(sm, sanitize=False)
if mol is None:
vals.append(0.0)
else:
try:
val = QED.default(mol)
vals.append(val)
except ValueError:
vals.append(0.0)
else:
vals.append(0.0)
return vals
# ===========================
# Diversity
def batch_diversity(smiles):
scores = []
df = pd.DataFrame({'smiles': smiles})
PandasTools.AddMoleculeColumnToFrame(df, 'smiles', 'mol')
fps = [GetMorganFingerprintAsBitVect(m, 4, nBits=2048) for m in df['mol'] if m is not None]
for i in range(1, len(fps)):
scores.extend(DataStructs.BulkTanimotoSimilarity(fps[i], fps[:i], returnDistance=True))
return np.mean(scores)
# ===========================
# Solubility
def batch_solubility(smiles):
vals = []
for sm in smiles:
mol = Chem.MolFromSmiles(sm)
if mol is None:
vals.append(0.0)
else:
low_logp = -2.12178879609
high_logp = 6.0429063424
logp = Crippen.MolLogP(mol)
val = (logp - low_logp) / (high_logp - low_logp)
val = np.clip(val, 0.1, 1.0)
vals.append(val)
return vals
# ===========================
# Read synthesizability model
def readSAModel(filename='SA_score.pkl.gz'):
model_data = pickle.load(gzip.open(filename))
outDict = {}
for i in model_data:
for j in range(1, len(i)):
outDict[i[j]] = float(i[0])
SA_model = outDict
return SA_model
SA_model = readSAModel()
# ===========================
#synthesizability
def batch_SA(smiles):
vals = []
for sm in smiles:
mol = Chem.MolFromSmiles(sm)
if sm != '' and mol is not None and mol.GetNumAtoms() > 1:
# fragment score
fp = Chem.AllChem.GetMorganFingerprint(mol, 2)
fps = fp.GetNonzeroElements()
score1 = 0.
nf = 0
for bitId, v in fps.items():
nf += v
sfp = bitId
score1 += SA_model.get(sfp, -4) * v
score1 /= nf
# features score
nAtoms = mol.GetNumAtoms()
nChiralCenters = len(Chem.FindMolChiralCenters(mol, includeUnassigned=True))
ri = mol.GetRingInfo()
nSpiro = Chem.AllChem.CalcNumSpiroAtoms(mol)
nBridgeheads = Chem.AllChem.CalcNumBridgeheadAtoms(mol)
nMacrocycles = 0
for x in ri.AtomRings():
if len(x) > 8:
nMacrocycles += 1
sizePenalty = nAtoms**1.005 - nAtoms
stereoPenalty = math.log10(nChiralCenters + 1)
spiroPenalty = math.log10(nSpiro + 1)
bridgePenalty = math.log10(nBridgeheads + 1)
macrocyclePenalty = 0.
if nMacrocycles > 0:
macrocyclePenalty = math.log10(2)
score2 = 0. - sizePenalty - stereoPenalty - spiroPenalty - bridgePenalty - macrocyclePenalty
score3 = 0.
if nAtoms > len(fps):
score3 = math.log(float(nAtoms) / len(fps)) * .5
sascore = score1 + score2 + score3
min = -4.0
max = 2.5
sascore = 11. - (sascore - min + 1) / (max - min) * 9.
# smooth the 10-end
if sascore > 8.:
sascore = 8. + math.log(sascore + 1. - 9.)
if sascore > 10.:
sascore = 10.0
elif sascore < 1.:
sascore = 1.0
val = (sascore - 5) / (1.5 - 5)
val = np.clip(val, 0.1, 1.0)
vals.append(val)
else:
vals.append(0.0)
return vals
# ===========================
# Read DRD2 model
DRD2_model = pickle.load(open('DRD2_score.sav', 'rb'))
def batch_DRD2(smiles):
vals = []
for sm in smiles:
mol = Chem.MolFromSmiles(sm)
if len(sm) != 0 and mol:
try:
morgan = [GetMorganFingerprintAsBitVect(mol, 2, 2048)]
val = DRD2_model.predict_proba(np.array(morgan))[:, 1]
val = val[0]
vals.append(val)
except ValueError:
vals.append(0.0)
else:
vals.append(0.0)
return vals