import pandas as pd import os import glob import argparse metric_max = ['dockq_score','lddt-lp','lddt-pli','gdt-ts','tm-score','lddt'] metric_min = ['irmsd','lrmsd','rmsd'] target_metrics_summary = {} target_metrics_summary['interface_protein_ligand'] = {'rmsd_lddt-pli_success_rate': [], 'lddt-lp': [], 'lddt-pli': []} target_metrics_summary['interface_protein_protein'] = {'dockq_score_success_rate': [], 'irmsd': [], 'lrmsd': [], 'lddt': []} target_metrics_summary['interface_antibody_antigen'] = {'dockq_score_success_rate': [], 'irmsd': [], 'lrmsd': [], 'lddt': []} target_metrics_summary['interface_protein_dna'] = {'dockq_score_success_rate': [], 'irmsd': [], 'lrmsd': [], 'lddt': []} target_metrics_summary['interface_protein_rna'] = {'dockq_score_success_rate': [], 'irmsd': [], 'lrmsd': [], 'lddt': []} target_metrics_summary['interface_protein_peptide'] = {'dockq_score_success_rate': [], 'irmsd': [], 'lrmsd': [], 'lddt': []} target_metrics_summary['monomer_protein'] = {'gdt-ts': [], 'tm-score': [], 'rmsd': [], 'lddt': []} target_metrics_summary['monomer_dna'] = {'gdt-ts': [], 'tm-score': [], 'rmsd': [], 'lddt': []} target_metrics_summary['monomer_rna'] = {'gdt-ts': [], 'tm-score': [], 'rmsd': [], 'lddt': []} def change_column_name(df): if 'native_chain_id_1' in df.columns and 'native_chain_id_2' in df.columns: df.rename(columns={'native_chain_id_1': 'interface_chain_id_1', 'native_chain_id_2': 'interface_chain_id_2'}, inplace=True) return df def find_overlap_sample(df, ref_df): if 'interface_chain_id_1' in df.columns and 'interface_chain_id_2' in df.columns: df_tuples = list(zip(df['pdb_id'], df['interface_chain_id_1'], df['interface_chain_id_2'])) ref_df_tuples = list(zip(ref_df['pdb_id'], ref_df['interface_chain_id_1'], ref_df['interface_chain_id_2'])) else: df_tuples = list(zip(df['pdb_id'])) ref_df_tuples = list(zip(ref_df['pdb_id'])) common_tuples = set(df_tuples).intersection(set(ref_df_tuples)) if 'interface_chain_id_1' in df.columns and 'interface_chain_id_2' in df.columns: mask = [tuple(row) in common_tuples for row in df[['pdb_id', 'interface_chain_id_1', 'interface_chain_id_2']].values] else: mask = [tuple(row) in common_tuples for row in df[['pdb_id']].values] df = df[mask] return df # Get best prediction for each target def get_best_rows(df, metric, metric_type): if metric_type == 'rank': if 'interface_chain_id_1' in df.columns and 'interface_chain_id_2' in df.columns: best_rows = df.loc[df.groupby(["pdb_id","interface_chain_id_1","interface_chain_id_2"])['ranking_score'].idxmax()] else: best_rows = df.loc[df.groupby(["pdb_id"])['ranking_score'].idxmax()] elif metric_type == 'best': if metric in metric_max: if 'interface_chain_id_1' in df.columns and 'interface_chain_id_2' in df.columns: best_rows = df.loc[df.groupby(["pdb_id","interface_chain_id_1","interface_chain_id_2"])[metric].idxmax()] else: best_rows = df.loc[df.groupby(["pdb_id"])[metric].idxmax()] elif metric in metric_min: if 'interface_chain_id_1' in df.columns and 'interface_chain_id_2' in df.columns: best_rows = df.loc[df.groupby(["pdb_id","interface_chain_id_1","interface_chain_id_2"])[metric].idxmin()] else: best_rows = df.loc[df.groupby(["pdb_id"])[metric].idxmin()] return best_rows def calculate_success_rate(df, metric, metric_type): """ Calculate success rate based on a metric and threshold. Args: df (pd.DataFrame): Input dataframe threshold (float): Threshold value for success Returns: float: Success rate """ if metric == 'dockq_score': df=df[df[metric].notna()] best_rows = get_best_rows(df, metric,metric_type) success_rate = (len(best_rows[best_rows[metric] >= 0.23])/len(best_rows))*100 elif metric == 'rmsd': df=df[df[metric].notna()] best_rows = get_best_rows(df, metric,metric_type) success_rate = (len(best_rows[best_rows[metric] < 2.0])/len(best_rows))*100 elif metric == 'rmsd_lddt-pli': df=df[df['rmsd'].notna() & df['lddt-pli'].notna()] # use rmsd to select best by default best_rows = get_best_rows(df, 'rmsd',metric_type) success_rate = (len(best_rows[(best_rows['rmsd']<2.0) & (best_rows['lddt-pli']>0.8)])/len(best_rows))*100 return success_rate def calculate_score_avg(df, metric, metric_type): """ Calculate success rate based on a metric and threshold. Args: df (pd.DataFrame): Input dataframe threshold (float): Threshold value for success Returns: float: Success rate """ df=df[df[metric].notna()] best_rows = get_best_rows(df, metric,metric_type) avg_score = best_rows[metric].mean() return avg_score def process_csv_files(evaluation_dir,target_dir,output_path,models,targets,metric_type): results = {} # Process interface files for model in models: results[model] = {} for target in targets: result_path = os.path.join(evaluation_dir,model,'raw',f"{target}_ost.csv") # if not os.path.exists(result_path): print(f'{result_path} not found') continue target_path = os.path.join(target_dir, f"{target}.csv") result_df = pd.read_csv(result_path) result_df = change_column_name(result_df) target_df = pd.read_csv(target_path) target_df = change_column_name(target_df) print(f'{target} num: {len(target_df)}') result_df = find_overlap_sample(result_df,target_df) results[model][target] = {} # Process pp interface: dockq success_rate,irmsd,lrmsd,lddt if target in ["interface_protein_protein", "interface_protein_peptide", "interface_antibody_antigen","interface_protein_dna", "interface_protein_rna"]: results[model][target]['lddt'] = calculate_score_avg(result_df, 'lddt',metric_type) if target in ["interface_protein_dna", "interface_protein_rna"]: if os.path.exists(os.path.join(evaluation_dir,model,'raw', f"{target}_dockqv2.csv")): result_path_dockqv2 = os.path.join(evaluation_dir,model,'raw', f"{target}_dockqv2.csv") result_df_dockqv2 = pd.read_csv(result_path_dockqv2) result_df_dockqv2 = change_column_name(result_df_dockqv2) result_df_dockqv2 = find_overlap_sample(result_df_dockqv2,target_df) results[model][target]['dockq_score_success_rate'] = calculate_success_rate(result_df_dockqv2, 'dockq_score',metric_type) results[model][target]['irmsd'] = calculate_score_avg(result_df_dockqv2, 'irmsd',metric_type) results[model][target]['lrmsd'] = calculate_score_avg(result_df_dockqv2, 'lrmsd',metric_type) else: results[model][target]['dockq_score_success_rate'] = calculate_success_rate(result_df, 'dockq_score',metric_type) results[model][target]['irmsd'] = calculate_score_avg(result_df, 'irmsd',metric_type) results[model][target]['lrmsd'] = calculate_score_avg(result_df, 'lrmsd',metric_type) # Process pl interface: rmsd_lddt-pli success_rate,lddt-lp,lddt-pli elif target in ["interface_protein_ligand"]: results[model][target]['rmsd_lddt-pli_success_rate'] = calculate_success_rate(result_df, 'rmsd_lddt-pli',metric_type) results[model][target]['lddt-lp'] = calculate_score_avg(result_df, 'lddt-lp',metric_type) results[model][target]['lddt-pli'] = calculate_score_avg(result_df, 'lddt-pli',metric_type) # Process monomer: gdt_ts,tm-score,rmsd,lddt elif target in ["monomer_dna", "monomer_rna", "monomer_protein"]: if 'gdt_ts' in result_df.columns: results[model][target]['gdt-ts'] = calculate_score_avg(result_df, 'gdt_ts',metric_type) elif 'gdt-ts' in result_df.columns: results[model][target]['gdt-ts'] = calculate_score_avg(result_df, 'gdt-ts',metric_type) if 'tm-score' in result_df.columns: results[model][target]['tm-score'] = calculate_score_avg(result_df, 'tm-score',metric_type) elif 'tm_score' in result_df.columns: results[model][target]['tm-score'] = calculate_score_avg(result_df, 'tm_score',metric_type) results[model][target]['rmsd'] = calculate_score_avg(result_df, 'rmsd',metric_type) results[model][target]['lddt'] = calculate_score_avg(result_df, 'lddt',metric_type) return results if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument( "--evaluation_dir", required=False, default='./examples/outputs/evaluation', help="The dir with the evaluation files.", ) parser.add_argument( "--target_dir", required=False, default='./examples/targets', help="The dir with the target files.", ) parser.add_argument( "--output_path", required=False, default='./examples/outputs/summary_table.csv', help="output path", ) parser.add_argument( "--algorithm_names", required=False, default= ['Protenix'], nargs='+', help="models to evaluate", ) parser.add_argument( "--targets", required=False, default= ["interface_protein_ligand","interface_antibody_antigen","interface_protein_dna", "monomer_protein"], nargs='+', help="targets to evaluate.", ) parser.add_argument( "--metric_type", required=False, default= "rank", help="rank or best", ) args = parser.parse_args() results = process_csv_files(args.evaluation_dir,args.target_dir,args.output_path,args.algorithm_names,args.targets,args.metric_type) for target in args.targets: for metric in target_metrics_summary[target].keys(): for model in args.algorithm_names: if target not in results[model].keys(): print(f'{model}: {target} not found') continue if metric in results[model][target].keys(): target_metrics_summary[target][metric].append(results[model][target][metric]) else: target_metrics_summary[target][metric].append(None) # Create a DataFrame with models as columns df_wide = pd.DataFrame() for target in target_metrics_summary.keys(): for metric in target_metrics_summary[target].keys(): row_data = { 'target': target, 'metric': metric } # Add model values as columns for i, model in enumerate(args.algorithm_names): if i < len(target_metrics_summary[target][metric]) and target_metrics_summary[target][metric][i] is not None: row_data[model] = round(target_metrics_summary[target][metric][i],2) else: row_data[model] = None if row_data[model] is not None: df_wide = pd.concat([df_wide, pd.DataFrame([row_data])], ignore_index=True) # Save the wide format DataFrame df_wide.to_csv(args.output_path, index=False) # Print the table in a nicely formatted way print("\nResults Summary:") print("=" * 80) # Group by target and print each group for target in df_wide['target'].unique(): print(f"\n{target.upper()}") print("-" * 80) # Get rows for this target target_df = df_wide[df_wide['target'] == target] # Print header header = "Metric".ljust(20) for model in args.algorithm_names: header += f"{model}".rjust(15) print(header) print("-" * 80) # Print each metric row for _, row in target_df.iterrows(): metric_str = str(row['metric']).ljust(20) for model in args.algorithm_names: value = row[model] if pd.isna(value): metric_str += "N/A".rjust(15) else: metric_str += f"{value:.2f}".rjust(15) print(metric_str) print("\n" + "=" * 80)