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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)
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