Digital Trials pipeline configured for WES

Source-only snapshot of the cluster branch for WES execution. Large
reference files (HPA/MANE/ensemble FASTA, model weights, ~597 MB) are
omitted: they are baked into the container images at build time and
mounted from the dreamdock-data PVC at runtime, and exceed the Gitea
request size limit.

Pipeline entry point is main.nf, which orchestrates the biotransformer,
conplex and tissue modules as a single workflow. Ligand inputs are read
from the eureka workspace; protein_zarr and chembl_db come from the
dreamdock-data PVC.

nf_metabol_screen_adaptive/app/convert.py

@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+
+import argparse
+import pandas as pd
+import sys
+from pathlib import Path
+
+def create_smiles_file(drug_csv_path, metabolite_file_path, output_path="smiles.smi"):
+    """
+    Create smiles.smi file by combining drug CSV and metabolite file.
+    
+    Args:
+        drug_csv_path: Path to CSV file with 'SMILES' header
+        metabolite_file_path: Path to tab-separated file with SMILES and metabolite IDs
+        output_path: Output file path (default: smiles.smi)
+    """
+    
+    # Convert to Path objects for better handling
+    drug_csv_path = Path(drug_csv_path)
+    metabolite_file_path = Path(metabolite_file_path)
+    output_path = Path(output_path)
+    
+    # Check if input files exist
+    if not drug_csv_path.exists():
+        raise FileNotFoundError(f"Drug CSV file not found: {drug_csv_path}")
+    
+    if not metabolite_file_path.exists():
+        raise FileNotFoundError(f"Metabolite file not found: {metabolite_file_path}")
+    
+    # Read drug CSV
+    try:
+        drug_df = pd.read_csv(drug_csv_path)
+    except Exception as e:
+        raise Exception(f"Error reading drug CSV file: {e}")
+    
+    # Validate drug CSV has SMILES column
+    if 'SMILES' not in drug_df.columns:
+        raise ValueError("Drug CSV file must have 'SMILES' column")
+    
+    # Check if metabolite file contains "NO_METABOLITES"
+    try:
+        with open(metabolite_file_path, 'r') as f:
+            first_line = f.readline().strip()
+    except Exception as e:
+        raise Exception(f"Error reading metabolite file: {e}")
+    
+    # Handle no metabolites case
+    if first_line == "NO_METABOLITES":
+        print("No metabolites found, creating output file with drugs only")
+        
+        # Create drug dataframe with drug_N format
+        drug_output = drug_df.copy()
+        drug_output['ID'] = [f'drug_{i}' for i in range(len(drug_df))]
+        drug_output = drug_output[['SMILES', 'ID']]
+        
+        # Write only drugs to output file
+        try:
+            drug_output.to_csv(output_path, sep='\t', header=False, index=False)
+        except Exception as e:
+            raise Exception(f"Error writing output file: {e}")
+        
+        print(f"Successfully created {output_path}")
+        print(f"  - {len(drug_df)} drugs")
+        print(f"  - 0 metabolites")
+        return
+    
+    # Read metabolite file normally (tab-separated, no header)
+    try:
+        metabolite_df = pd.read_csv(metabolite_file_path, sep='\t', header=None, names=['SMILES', 'ID'])
+    except Exception as e:
+        raise Exception(f"Error reading metabolite file: {e}")
+    
+    # Validate metabolite file has at least 2 columns
+    if metabolite_df.shape[1] < 2:
+        raise ValueError("Metabolite file must have at least 2 columns (SMILES and ID)")
+    
+    # Create drug dataframe with drug_N format
+    drug_output = drug_df.copy()
+    drug_output['ID'] = [f'drug_{i}' for i in range(len(drug_df))]
+    drug_output = drug_output[['SMILES', 'ID']]
+    
+    # Use metabolite dataframe as-is (already has SMILES and ID columns)
+    metabolite_output = metabolite_df[['SMILES', 'ID']]
+    
+    # Combine drug and metabolite dataframes
+    combined_df = pd.concat([drug_output, metabolite_output], ignore_index=True)
+    
+    # Write to output file (tab-separated, no header)
+    try:
+        combined_df.to_csv(output_path, sep='\t', header=False, index=False)
+    except Exception as e:
+        raise Exception(f"Error writing output file: {e}")
+    
+    print(f"Successfully created {output_path}")
+    print(f"  - {len(drug_df)} drugs")
+    print(f"  - {len(metabolite_df)} metabolites")
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Create smiles.smi file by combining drug CSV and metabolite file",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python3 create_smiles.py drug.csv metabolites.txt
+  python3 create_smiles.py drug.csv metabolites.txt -o compounds.smi
+        """
+    )
+    
+    parser.add_argument(
+        'drug_csv', 
+        help='Path to CSV file with SMILES column containing drug compounds'
+    )
+    
+    parser.add_argument(
+        'metabolite_file', 
+        help='Path to tab-separated file with SMILES and metabolite IDs'
+    )
+    
+    parser.add_argument(
+        '-o', '--output', 
+        default='smiles.smi',
+        help='Output file path (default: smiles.smi)'
+    )
+    
+    args = parser.parse_args()
+    
+    try:
+        create_smiles_file(args.drug_csv, args.metabolite_file, args.output)
+    except Exception as e:
+        print(f"Error: {e}", file=sys.stderr)
+        sys.exit(1)
+
+if __name__ == "__main__":
+    main()