first commit

.gitignore

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+admet_ai
+*.sdf

README.md

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+## 使用admet-ai预测
+
+```bash
+git clone https://github.com/swansonk14/admet_ai
+cd admet_ai
+micromamba create -n admet_ai python=3.10
+micromamba activate admet_ai
+pip install -r requirements.txt
+pip install -e .
+python fix_admet_load.py --data_path ./data/molecules.csv --save_path ./data/preds.csv --smiles_column smiles
+```

fix_admet_load.py

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+import torch
+import sys
+import os
+import argparse
+from pathlib import Path
+
+def patch_torch_load():
+    """
+    修补torch.load函数，添加weights_only=False参数
+    这是为了解决PyTorch 2.6版本中weights_only默认值变更导致的问题
+    """
+    original_torch_load = torch.load
+    
+    def patched_load(f, map_location=None, pickle_module=None, **pickle_load_args):
+        # 显式添加weights_only=False参数
+        if 'weights_only' not in pickle_load_args:
+            pickle_load_args['weights_only'] = False
+        return original_torch_load(f, map_location, pickle_module, **pickle_load_args)
+    
+    torch.load = patched_load
+    print("已修补torch.load函数，添加weights_only=False参数")
+
+def main():
+    parser = argparse.ArgumentParser(description='运行admet_predict并修复torch.load问题')
+    parser.add_argument('--data_path', type=str, required=True, help='输入数据路径')
+    parser.add_argument('--save_path', type=str, required=True, help='保存结果路径')
+    parser.add_argument('--smiles_column', type=str, default='smiles', help='SMILES列名')
+    parser.add_argument('--models_dir', type=str, help='模型目录')
+    parser.add_argument('--include_physchem', action='store_true', help='是否包含物理化学特性')
+    parser.add_argument('--drugbank_path', type=str, help='DrugBank路径')
+    parser.add_argument('--atc_code', type=str, help='ATC代码')
+    
+    args, unknown = parser.parse_known_args()
+    
+    # 应用修补
+    patch_torch_load()
+    
+    # 构建admet_predict命令
+    cmd = [sys.executable, '-m', 'admet_ai.admet_predict']
+    cmd.extend(['--data_path', args.data_path])
+    cmd.extend(['--save_path', args.save_path])
+    cmd.extend(['--smiles_column', args.smiles_column])
+    
+    if args.models_dir:
+        cmd.extend(['--models_dir', args.models_dir])
+    if args.include_physchem:
+        cmd.append('--include_physchem')
+    if args.drugbank_path:
+        cmd.extend(['--drugbank_path', args.drugbank_path])
+    if args.atc_code:
+        cmd.extend(['--atc_code', args.atc_code])
+    
+    # 添加未知参数
+    cmd.extend(unknown)
+    
+    # 执行命令
+    os.execv(sys.executable, cmd)
+
+if __name__ == '__main__':
+    main()

scripts/sdf_to_smiles.py

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+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+
+import os
+from rdkit import Chem
+
+# 当前目录路径
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+# 输入SDF文件路径
+sdf_file = os.path.join(current_dir, 'fgbar_vina_SP_1_pv.sdf')
+
+# 输出SMILES文件路径
+smiles_file = os.path.join(current_dir, 'molecules.txt')
+
+def sdf_to_smiles(sdf_path, output_path):
+    """读取SDF文件中的分子并将其转换为SMILES格式保存到文本文件"""
+    # 读取SDF文件
+    suppl = Chem.SDMolSupplier(sdf_path)
+    
+    # 计数有效分子数量
+    valid_mol_count = 0
+    
+    # 打开输出文件
+    with open(output_path, 'w') as f:
+        # 遍历所有分子
+        for i, mol in enumerate(suppl):
+            if mol is not None:  # 确保分子有效
+                # 获取SMILES
+                smiles = Chem.MolToSmiles(mol)
+                # 写入文件
+                f.write(f"{smiles}\n")
+                valid_mol_count += 1
+    
+    return valid_mol_count
+
+def main():
+    print(f"正在读取SDF文件: {sdf_file}")
+    mol_count = sdf_to_smiles(sdf_file, smiles_file)
+    print(f"成功处理了 {mol_count} 个分子")
+    print(f"SMILES已保存到: {smiles_file}")
+
+if __name__ == "__main__":
+    main()