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predict_item.py
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import argparse
import pandas as pd
from collections import OrderedDict
from rdkit import Chem
from rdkit.Chem import MolFromSmiles
import networkx as nx
from utils import *
from tqdm import tqdm
import sys, os
import torch
from model import Model
import datetime
import csv
import time
# os.environ["CUDA_VISIBLE_DEVICES"] = "2"
import warnings
warnings.filterwarnings("ignore")
def logger(function):
def wrapper(*args, **kwargs):
"""Record the start and end time of the function and the running time of the function"""
start = datetime.datetime.now()
print(f"----- {function.__name__}: start -----{start}")
output = function(*args, **kwargs)
print(
f"----- {function.__name__}: end -----{datetime.datetime.now()}\n-----cost time: {datetime.datetime.now() - start}")
return output
return wrapper
def atom_features(atom):
return np.array(one_of_k_encoding_unk(atom.GetSymbol(),
['C', 'N', 'O', 'S', 'F', 'Si', 'P', 'Cl', 'Br', 'Mg', 'Na', 'Ca', 'Fe', 'As',
'Al', 'I', 'B', 'V', 'K', 'Tl', 'Yb', 'Sb', 'Sn', 'Ag', 'Pd', 'Co', 'Se',
'Ti', 'Zn', 'H', 'Li', 'Ge', 'Cu', 'Au', 'Ni', 'Cd', 'In', 'Mn', 'Zr', 'Cr',
'Pt', 'Hg', 'Pb', 'Unknown']) +
one_of_k_encoding(atom.GetDegree(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) +
one_of_k_encoding_unk(atom.GetTotalNumHs(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) +
one_of_k_encoding_unk(atom.GetImplicitValence(), [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]) +
[atom.GetIsAromatic()])
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 list(map(lambda s: x == s, allowable_set))
# 查找原子是否在允许的字符集中，若不在则为'Unknown'，返回one-hot编码格式的列表
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 list(map(lambda s: x == s, allowable_set))
def smile_to_graph(smile):
mol = Chem.MolFromSmiles(smile)
c_size = mol.GetNumAtoms() # 获取所有原子数目
features = []
for atom in mol.GetAtoms():
feature = atom_features(atom)
features.append(feature / sum(feature))
edges = []
tmp = []
for bond in mol.GetBonds():
edges.append([bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()])
tmp.append([bond.GetBeginAtomIdx(), bond.GetEndAtomIdx()])
g = nx.Graph(edges).to_directed()
edge_index = []
for e1, e2 in g.edges:
edge_index.append([e1, e2])
return c_size, features, edge_index
def getSmilegraph(compound_iso_smiles):
compound_iso_smiles = set(compound_iso_smiles)
smile_graph = {}
for smile in compound_iso_smiles:
g = smile_to_graph(smile)
smile_graph[smile] = g
return smile_graph
def formatSmiles(smileslist):
formatSimle = []
for simles in smileslist:
formatSimle.append(Chem.MolToSmiles(Chem.MolFromSmiles(simles), isomericSmiles=True))
return formatSimle
def createData(smi, target_name):
PROTEIN_FILE = script_directory + "/data/" + target_name + ".npz"
protein = np.load(PROTEIN_FILE)['arr_0'].astype('float16')
test_drugs = [smi]
test_Y = [-100 for _ in test_drugs]
test_prots = [protein for _ in test_drugs]
test_drugs, test_prots, test_Y = np.asarray(test_drugs), np.asarray(test_prots), np.asarray(test_Y)
compound_iso_smiles = []
compound_iso_smiles.extend(test_drugs)
test_drugs = formatSmiles(test_drugs)
compound_iso_smiles = formatSmiles(compound_iso_smiles)
smile_graph = getSmilegraph(compound_iso_smiles)
test_data = TestbedDataset(root='data', dataset=target_name + '_test', xd=test_drugs, xt=test_prots, y=test_Y,
smile_graph=smile_graph)
return test_data
def predicting(model, device, loader):
model.eval()
total_preds = torch.Tensor()
with torch.no_grad():
for batch_idx, data in enumerate(loader):
data = data.to(device)
output = model(data)
total_preds = torch.cat((total_preds, output.cpu()), 0)
# return output.numpy()
return total_preds.numpy().flatten()
# @logger
def MiFunc(smi, target_name, device):
test_data = createData(smi, target_name)
test_loader = DataLoader(test_data, batch_size=256, shuffle=False)
model = Model().to(device)
path_checkpoint = script_directory + "/model/model.model" # 模型加载路径
checkpoint = torch.load(path_checkpoint) # 加载模型
model.load_state_dict(checkpoint) # 加载模型可学习参数
P = predicting(model, device, test_loader)[0]
return P
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='compute molecule affinity')
parser.add_argument('--input_file', type=str, required=True,
help='Input file path, please provide your data path(absolute or relative path), the file is a CSV file, and the required header is "Smiles"')
parser.add_argument('--output_file', type=str, required=True,
help='Output file path, please provide your data path(absolute or relative path), the file is a CSV file')
# Micro
args = parser.parse_args()
cuda_name = "cuda:0"
device = torch.device(cuda_name if torch.cuda.is_available() else "cpu")
target_name = '5vd0'
# df = pd.read_csv("./data/Smiles800W.csv")
# smiles_list = df["Smiles"].tolist()
# smiles = 'CN(C)CC[C@@H](c1ccc(Br)cc1)c1ccccn1'
input_file_name = args.input_file
assert input_file_name.endswith(".csv"), "Please check your input, end with '.csv'."
df = pd.read_csv(input_file_name)
smiles_list = list(df['Smiles'])
# smiles_list = ["CCC", "CCCCC", "COCCC"]
in_l = []
out_l = []
for item in smiles_list:
try:
P = MiFunc(item, target_name, device)
print(P)
in_l.append(item)
out_l.append(P)
time.sleep(0.3)
except:
print("SMILES ERROR")
continue
result = pd.DataFrame()
result['Smiles'] = in_l
result['affinity'] = out_l
output_file_path = args.output_file
assert output_file_path.endswith(".csv"), "Please check your output, end with '.csv'."
result.to_csv(output_file_path, index=False)