first add

scripts/batch_process.py

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+"""
+Batch processing script for analyzing all macrolactones in the dataset.
+"""
+import sys
+sys.path.append('..')
+
+import pandas as pd
+from pathlib import Path
+from tqdm import tqdm
+import json
+from rdkit import Chem
+
+from src.fragment_cleaver import process_molecule
+from src.fragment_dataclass import MoleculeFragments
+
+
+def batch_process_molecules(csv_path: str, output_base_dir: str, 
+                            max_molecules: int = None):
+    """
+    Process all molecules in the CSV file.
+    
+    Args:
+        csv_path: Path to the CSV file containing SMILES
+        output_base_dir: Base directory for output
+        max_molecules: Maximum number of molecules to process (None for all)
+    """
+    # Read CSV
+    df = pd.read_csv(csv_path)
+    print(f"Loaded {len(df)} molecules from {csv_path}")
+    
+    if max_molecules:
+        df = df.head(max_molecules)
+        print(f"Processing first {max_molecules} molecules")
+    
+    # Create output directory
+    output_dir = Path(output_base_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    # Statistics
+    successful = 0
+    failed = 0
+    failed_molecules = []
+    all_fragments = []
+    
+    # Process each molecule
+    for idx, row in tqdm(df.iterrows(), total=len(df), desc="Processing molecules"):
+        smiles = row['smiles']
+        molecule_id = row.get('IDs', f'molecule_{idx}')
+        
+        try:
+            # Process molecule
+            mol_fragments = process_molecule(smiles, idx)
+            
+            if mol_fragments is None or len(mol_fragments.fragments) == 0:
+                failed += 1
+                failed_molecules.append({
+                    'index': idx,
+                    'id': molecule_id,
+                    'reason': 'No fragments extracted'
+                })
+                continue
+            
+            # Create output directory for this molecule
+            mol_output_dir = output_dir / mol_fragments.parent_id
+            mol_output_dir.mkdir(parents=True, exist_ok=True)
+            
+            # Save complete molecule fragments
+            mol_fragments_path = mol_output_dir / f"{mol_fragments.parent_id}_all_fragments.json"
+            mol_fragments.to_json_file(str(mol_fragments_path))
+            
+            # Save individual fragments
+            for frag in mol_fragments.fragments:
+                frag_path = mol_output_dir / f"{frag.fragment_id}.json"
+                frag.to_json_file(str(frag_path))
+                
+                # Collect for overall statistics
+                all_fragments.append({
+                    'parent_id': frag.parent_id,
+                    'fragment_id': frag.fragment_id,
+                    'cleavage_position': frag.cleavage_position,
+                    'fragment_smiles': frag.fragment_smiles,
+                    'atom_count': frag.atom_count,
+                    'molecular_weight': frag.molecular_weight,
+                    'parent_smiles': frag.parent_smiles
+                })
+            
+            successful += 1
+            
+        except Exception as e:
+            failed += 1
+            failed_molecules.append({
+                'index': idx,
+                'id': molecule_id,
+                'error': str(e)
+            })
+            print(f"\nError processing molecule {idx} ({molecule_id}): {e}")
+    
+    # Save overall statistics
+    stats = {
+        'total_molecules': len(df),
+        'successful': successful,
+        'failed': failed,
+        'total_fragments': len(all_fragments),
+        'failed_molecules': failed_molecules
+    }
+    
+    stats_path = output_dir / 'processing_stats.json'
+    with open(stats_path, 'w') as f:
+        json.dump(stats, f, indent=2)
+    
+    # Save all fragments as CSV for easy analysis
+    if all_fragments:
+        fragments_df = pd.DataFrame(all_fragments)
+        fragments_csv_path = output_dir / 'all_fragments.csv'
+        fragments_df.to_csv(fragments_csv_path, index=False)
+        print(f"\n✓ Saved all fragments to: {fragments_csv_path}")
+    
+    # Print summary
+    print(f"\n{'='*60}")
+    print(f"PROCESSING COMPLETE")
+    print(f"{'='*60}")
+    print(f"Total molecules: {len(df)}")
+    print(f"Successfully processed: {successful}")
+    print(f"Failed: {failed}")
+    print(f"Total fragments extracted: {len(all_fragments)}")
+    print(f"{'='*60}")
+    print(f"\nResults saved to: {output_dir}")
+    print(f"Statistics saved to: {stats_path}")
+    
+    return fragments_df if all_fragments else None
+
+
+if __name__ == "__main__":
+    import argparse
+    
+    parser = argparse.ArgumentParser(
+        description="Batch process macrolactones to extract side chain fragments"
+    )
+    parser.add_argument(
+        '--input',
+        type=str,
+        default='../ring16/temp.csv',
+        help='Input CSV file path'
+    )
+    parser.add_argument(
+        '--output',
+        type=str,
+        default='../output/fragments',
+        help='Output directory path'
+    )
+    parser.add_argument(
+        '--max',
+        type=int,
+        default=None,
+        help='Maximum number of molecules to process (default: all)'
+    )
+    
+    args = parser.parse_args()
+    
+    batch_process_molecules(
+        csv_path=args.input,
+        output_base_dir=args.output,
+        max_molecules=args.max
+    )
+