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mol_featurizer.py
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mol_featurizer.py
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# -*- coding: utf-8 -*-
"""
@Time:Created on 2019/4/30 14:19
@author: LiFan Chen
@Filename: mol_featurizer.py
@Software: PyCharm
"""
import numpy as np
import joblib
from joblib import parallel_backend, Parallel, delayed
from tqdm import tqdm
from rdkit import Chem
from rdkit.Chem import Descriptors
num_atom_feat = 34
def one_of_k_encoding(x, allowable_set):
if x not in allowable_set:
raise Exception("input {0} not in allowable set{1}:".format(
x, allowable_set))
return [x == s for s in allowable_set]
def one_of_k_encoding_unk(x, allowable_set):
"""Maps inputs not in the allowable set to the last element."""
if x not in allowable_set:
x = allowable_set[-1]
return [x == s for s in allowable_set]
def atom_features(atom,explicit_H=False,use_chirality=True):
"""Generate atom features including atom symbol(10),degree(7),formal charge,
radical electrons,hybridization(6),aromatic(1),Chirality(3)
"""
symbol = ['C', 'N', 'O', 'F', 'P', 'S', 'Cl', 'Br', 'I', 'other'] # 10-dim
degree = [0, 1, 2, 3, 4, 5, 6] # 7-dim
hybridizationType = [Chem.rdchem.HybridizationType.SP,
Chem.rdchem.HybridizationType.SP2,
Chem.rdchem.HybridizationType.SP3,
Chem.rdchem.HybridizationType.SP3D,
Chem.rdchem.HybridizationType.SP3D2,
'other'] # 6-dim
results = one_of_k_encoding_unk(atom.GetSymbol(),symbol) + \
one_of_k_encoding(atom.GetDegree(),degree) + \
[atom.GetFormalCharge(), atom.GetNumRadicalElectrons()] + \
one_of_k_encoding_unk(atom.GetHybridization(), hybridizationType) + [atom.GetIsAromatic()] # 10+7+2+6+1=26
# In case of explicit hydrogen(QM8, QM9), avoid calling `GetTotalNumHs`
if not explicit_H:
results = results + one_of_k_encoding_unk(atom.GetTotalNumHs(),
[0, 1, 2, 3, 4]) # 26+5=31
if use_chirality:
try:
results = results + one_of_k_encoding_unk(
atom.GetProp('_CIPCode'),
['R', 'S']) + [atom.HasProp('_ChiralityPossible')]
except:
results = results + [False, False] + [atom.HasProp('_ChiralityPossible')] # 31+3 =34
return results
def adjacent_matrix(mol):
adjacency = Chem.GetAdjacencyMatrix(mol)
return np.array(adjacency,dtype=np.float32)
def mol_features(smiles):
try:
mol = Chem.MolFromSmiles(smiles)
except:
raise RuntimeError("SMILES cannot been parsed!")
#mol = Chem.AddHs(mol)
atom_feat = np.zeros((mol.GetNumAtoms(), num_atom_feat))
for atom in mol.GetAtoms():
atom_feat[atom.GetIdx(), :] = atom_features(atom)
adj_matrix = adjacent_matrix(mol)
return atom_feat, adj_matrix
def construct_single_data(data):
smiles, sequence, interaction = data.strip().split(" ")
atom_feature, adj = mol_features(smiles)
protein_embedding = get_protein_embedding(model, seq_to_kmers(sequence))
label = np.array(interaction,dtype=np.float32)
atom_feature = torch.FloatTensor(atom_feature)
adj = torch.FloatTensor(adj)
protein = torch.FloatTensor(protein_embedding)
label = torch.FloatTensor(label)
return atom_feature, adj, protein, label
if __name__ == "__main__":
from word2vec import seq_to_kmers, get_protein_embedding
from gensim.models import Word2Vec
import os
import torch
import pickle
# DATASET = "davis"
with open('data/input/converted_all_data.csv',"r") as f: # ../data/kinase_tran.txt
#with open('data/input/kinase_dropna_clean_400.csv',"r") as f: # ../data/kinase_tran.txt
data_list = f.read().strip().split('\n')
N = len(data_list)
model = Word2Vec.load("word2vec_30.model")
# with parallel_backend('threading', n_jobs=joblib.cpu_count()):
# dataset = Parallel()(delayed(construct_single_data)(data) for no, data in enumerate(tqdm(data_list)))
compounds, adjacencies,proteins,interactions = [], [], [], []
for no, data in enumerate(tqdm(data_list)):
#print('/'.join(map(str, [no + 1, N])))
smiles, sequence, interaction = data.strip().split(" ")
try:
atom_feature, adj = mol_features(smiles)
protein_embedding = get_protein_embedding(model, seq_to_kmers(sequence))
except Exception as e:
print(e)
continue
label = np.array(interaction,dtype=np.float32)
atom_feature = torch.FloatTensor(atom_feature)
adj = torch.FloatTensor(adj)
protein = torch.FloatTensor(protein_embedding)
label = torch.FloatTensor(label)
compounds.append(atom_feature)
adjacencies.append(adj)
proteins.append(protein)
interactions.append(label)
dataset = list(zip(compounds, adjacencies, proteins, interactions))
with open("data/output/kinase_test.txt", "wb") as f:
joblib.dump(dataset, f)
# print('The preprocess of ' + DATASET + ' dataset has finished!')