File size: 4,019 Bytes
6b76845 | 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 | import os
import pickle
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from tqdm import tqdm
from unimol_tools import UniMolRepr
import os, sys, contextlib
@contextlib.contextmanager
def suppress_stdout_stderr():
"""with 块内所有 print / tqdm / warning 都不会显示"""
with open(os.devnull, 'w') as devnull:
old_out, old_err = sys.stdout, sys.stderr
sys.stdout, sys.stderr = devnull, devnull
try:
yield
finally:
sys.stdout, sys.stderr = old_out, old_err
def get_unimol_embeddings(smiles_list, output_file="UniMol_emb512.pkl",
model_name='unimolv1', model_size='84m',
remove_hs=False, batch_size=32):
"""
使用Uni-Mol模型为SMILES列表生成分子嵌入，并保存为pickle文件
参数:
smiles_list (list): SMILES字符串列表
output_file (str): 输出pickle文件路径
model_name (str): 模型名称，可选'unimolv1'或'unimolv2'
model_size (str): 模型大小，仅在使用unimolv2时有效
remove_hs (bool): 是否移除氢原子
batch_size (int): 批处理大小
返回:
dict: 包含SMILES及其对应嵌入的字典
"""
# 初始化模型
clf = UniMolRepr(
data_type='molecule',
remove_hs=remove_hs,
model_name=model_name,
model_size=model_size
)
# 用于存储结果的字典
embeddings_dict = {}
error_smiles = []
# 批处理SMILES
total_batches = (len(smiles_list) + batch_size - 1) // batch_size
print(f"开始生成{len(smiles_list)}个SMILES的嵌入表示...")
print("开始")
# with suppress_stdout_stderr():
for i in tqdm(range(total_batches), desc="处理批次"):
batch = smiles_list[i*batch_size : (i+1)*batch_size]
try:
# 获取嵌入表示
batch_repr = clf.get_repr(batch, return_atomic_reprs=False)
# 将结果存入字典 (使用CLS token作为分子表示)
for idx, smiles in enumerate(batch):
embeddings_dict[smiles] = batch_repr['cls_repr'][idx]
except Exception as e:
print(f"处理批次 {i+1}/{total_batches} 时发生错误: {str(e)}")
# 记录处理失败的SMILES
error_smiles.extend(batch)
# 保存嵌入结果
with open(output_file, 'wb') as f:
pickle.dump(embeddings_dict, f)
print(f"嵌入生成完成！共处理 {len(smiles_list)} 个SMILES，"
f"{len(smiles_list) - len(error_smiles)} 个成功，"
f"{len(error_smiles)} 个失败。")
print(f"嵌入结果已保存至 {output_file}")
if error_smiles:
print(f"处理失败的SMILES已记录。")
return embeddings_dict
# 使用示例
if __name__ == "__main__":
# 假设unique_smiles是你的SMILES列表
unique_smiles = [
"CC(=O)OC1=CC=CC=C1C(=O)O", # Aspirin
"CN1C=NC2=C1C(=O)N(C(=O)N2C)C" # Caffeine
] # 示例SMILES列表
unique_smiles = pd.read_csv('./LINCS2020/LINCS2020_smiles.csv')['SMILES'].tolist()
# UniMol V1
# embeddings = get_unimol_embeddings(
# smiles_list=unique_smiles,
# output_file="embeddings/UniMol_emb512.pkl",
# model_name='unimolv1',
# model_size='84m',
# remove_hs=False,
# batch_size=32
# )
# UniMol V2
embeddings = get_unimol_embeddings(
smiles_list=unique_smiles,
output_file="embeddings/UniMolV2_emb1024.pkl", # save path
model_name='unimolv2', # 修改为 v2 版本
model_size='310m', # 指定 v2 模型大小（根据需要选择）
remove_hs=False,
batch_size=32
)
# 打印样例嵌入
sample_smiles = unique_smiles[0]
print(f"SMILES: {sample_smiles}")
print(f"嵌入向量: {embeddings[sample_smiles]}")
print(f"嵌入维度: {len(embeddings[sample_smiles])}")
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