finsh merge all results

scripts/analyze_results.py

@@ -1,151 +0,0 @@
-#!/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()

scripts/merge_csv.py

@@ -0,0 +1,23 @@
+# 合并 COCOUNT 与 plant 的对接结果。
+from pathlib import Path
+import pandas as pd
+
+data = {
+    "plant": ["qed_values_poses_trpe_converted.csv", "qed_values_poses_fgbar_converted.csv"],
+    "cocount": ["qed_values_trpe.csv", "qed_values_fgbar.csv"]
+}
+
+df_trpe_plant1 = pd.read_csv(data["plant"][0])
+df_fgbar_plant2 = pd.read_csv(data["plant"][1])
+df_trpe_plant1["source"] = "plant"
+df_fgbar_plant2["source"] = "plant"
+df_trpe_cocount1 = pd.read_csv(data["cocount"][0])
+df_fgbar_cocount2 = pd.read_csv(data["cocount"][1])
+df_trpe_cocount1["source"] = "cocount"
+df_fgbar_cocount2["source"] = "cocount"
+
+df_trpe = pd.concat([df_trpe_plant1, df_trpe_cocount1])
+df_fgbar = pd.concat([df_fgbar_plant2, df_fgbar_cocount2])
+
+df_trpe.to_csv("qed_values_poses_trpe_all.csv", index=False)
+df_fgbar.to_csv("qed_values_poses_fgbar_all.csv", index=False) # 41166 条数据