update

scripts/analyze_results.py

@@ -0,0 +1,151 @@
+#!/usr/bin/env python3  
+"""  
+分析AutoDock Vina对接结果脚本  
+用法: python analyze_results.py poses_directory output_csv  
+"""  
+  
+import os  
+import sys  
+import pandas as pd  
+import re  
+from pathlib import Path  
+  
+def parse_vina_output(pdbqt_file):  
+    """解析Vina输出文件，提取能量信息"""  
+    results = []  
+      
+    try:  
+        with open(pdbqt_file, 'r') as f:  
+            lines = f.readlines()  
+          
+        ligand_name = Path(pdbqt_file).stem.replace('_out', '')  
+        model_num = 0  
+          
+        for line in lines:  
+            if line.startswith('REMARK VINA RESULT:'):  
+                # 解析能量信息  
+                # 格式: REMARK VINA RESULT: -8.5 0.000 0.000  
+                parts = line.strip().split()  
+                if len(parts) >= 4:  
+                    binding_energy = float(parts[3])  
+                    rmsd_lb = float(parts[4]) if len(parts) > 4 else 0.0  
+                    rmsd_ub = float(parts[5]) if len(parts) > 5 else 0.0  
+                      
+                    model_num += 1  
+                    results.append({  
+                        'ligand_name': ligand_name,  
+                        'model': model_num,  
+                        'binding_energy': binding_energy,  
+                        'rmsd_lb': rmsd_lb,  
+                        'rmsd_ub': rmsd_ub,  
+                        'file_path': pdbqt_file  
+                    })  
+      
+    except Exception as e:  
+        print(f"解析文件失败 {pdbqt_file}: {e}")  
+      
+    return results  
+  
+def analyze_poses_directory(poses_dir):  
+    """分析整个poses目录"""  
+    all_results = []  
+      
+    poses_path = Path(poses_dir)  
+    if not poses_path.exists():  
+        print(f"错误: 目录不存在 {poses_dir}")  
+        return []  
+      
+    # 查找所有_out.pdbqt文件  
+    pdbqt_files = list(poses_path.glob("*_out.pdbqt"))  
+      
+    print(f"找到 {len(pdbqt_files)} 个结果文件")  
+      
+    for pdbqt_file in pdbqt_files:  
+        results = parse_vina_output(str(pdbqt_file))  
+        all_results.extend(results)  
+      
+    return all_results  
+  
+def generate_summary_stats(df):  
+    """生成汇总统计信息"""  
+    if df.empty:  
+        return {}  
+      
+    # 获取每个配体的最佳结合能  
+    best_poses = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()]  
+      
+    stats = {  
+        'total_ligands': df['ligand_name'].nunique(),  
+        'total_poses': len(df),  
+        'best_binding_energy': df['binding_energy'].min(),  
+        'worst_binding_energy': df['binding_energy'].max(),  
+        'mean_binding_energy': df['binding_energy'].mean(),  
+        'median_binding_energy': df['binding_energy'].median(),  
+        'std_binding_energy': df['binding_energy'].std(),  
+        'ligands_better_than_minus_7': (best_poses['binding_energy'] < -7.0).sum(),  
+        'ligands_better_than_minus_8': (best_poses['binding_energy'] < -8.0).sum(),  
+        'ligands_better_than_minus_9': (best_poses['binding_energy'] < -9.0).sum(),  
+    }  
+      
+    return stats  
+  
+def main():  
+    if len(sys.argv) != 3:  
+        print("用法: python analyze_results.py <poses目录> <输出CSV文件>")  
+        sys.exit(1)  
+      
+    poses_dir = sys.argv[1]  
+    output_csv = sys.argv[2]  
+      
+    print("开始分析对接结果...")  
+      
+    # 分析所有结果  
+    results = analyze_poses_directory(poses_dir)  
+      
+    if not results:  
+        print("没有找到有效的结果文件")  
+        sys.exit(1)  
+      
+    # 转换为DataFrame  
+    df = pd.DataFrame(results)  
+      
+    # 保存详细结果  
+    df.to_csv(output_csv, index=False)  
+    print(f"详细结果已保存到: {output_csv}")  
+      
+    # 生成汇总统计  
+    stats = generate_summary_stats(df)  
+      
+    # 打印汇总信息  
+    print("\n=== 对接结果汇总 ===")  
+    print(f"总配体数量: {stats['total_ligands']}")  
+    print(f"总构象数量: {stats['total_poses']}")  
+    print(f"最佳结合能: {stats['best_binding_energy']:.2f} kcal/mol")  
+    print(f"最差结合能: {stats['worst_binding_energy']:.2f} kcal/mol")  
+    print(f"平均结合能: {stats['mean_binding_energy']:.2f} kcal/mol")  
+    print(f"中位结合能: {stats['median_binding_energy']:.2f} kcal/mol")  
+    print(f"标准差: {stats['std_binding_energy']:.2f} kcal/mol")  
+    print(f"结合能 < -7.0 的配体: {stats['ligands_better_than_minus_7']}")  
+    print(f"结合能 < -8.0 的配体: {stats['ligands_better_than_minus_8']}")  
+    print(f"结合能 < -9.0 的配体: {stats['ligands_better_than_minus_9']}")  
+      
+    # 显示前10个最佳结果  
+    best_results = df.loc[df.groupby('ligand_name')['binding_energy'].idxmin()]  
+    best_results = best_results.sort_values('binding_energy').head(10)  
+      
+    print("\n=== 前10个最佳结果 ===")  
+    for _, row in best_results.iterrows():  
+        print(f"{row['ligand_name']}: {row['binding_energy']:.2f} kcal/mol")  
+      
+    # 保存汇总统计  
+    summary_file = output_csv.replace('.csv', '_summary.txt')  
+    with open(summary_file, 'w') as f:  
+        f.write("AutoDock Vina 对接结果汇总\n")  
+        f.write("=" * 30 + "\n\n")  
+        for key, value in stats.items():  
+            f.write(f"{key}: {value}\n")  
+      
+    print(f"\n汇总统计已保存到: {summary_file}")  
+  
+if __name__ == "__main__":  
+    main()