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notebooks/02_rdkit_substructure_matching.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# RDKit Substructure Matching with Multiprocessing\n",
+    "\n",
+    "This notebook performs parallel substructure matching on all extracted SDF files using SMARTS patterns with 220 processes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "from rdkit import Chem\n",
+    "from rdkit.Chem import SDMolSupplier, AllChem\n",
+    "from joblib import Parallel, delayed\n",
+    "import multiprocessing\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "\n",
+    "# Set up paths\n",
+    "sdf_dir = Path('../extracted_sdf_files')\n",
+    "results_dir = Path('../matching_results')\n",
+    "results_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "print(f\"SDF directory: {sdf_dir}\")\n",
+    "print(f\"Results directory: {results_dir}\")\n",
+    "\n",
+    "# Load the SDF file list\n",
+    "sdf_df = pd.read_csv(sdf_dir / 'sdf_file_list.csv')\n",
+    "print(f\"Found {len(sdf_df)} SDF files to process\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define SMARTS patterns for substructure matching\n",
+    "smarts_patterns = {\n",
+    "    'benzene_ring': 'c1ccccc1',\n",
+    "    'pyridine': 'c1ccncc1', \n",
+    "    'carboxylic_acid': 'C(=O)O',\n",
+    "    'alcohol': '[OX2H]',\n",
+    "    'amine': '[NX3;H2,H1;!$(NC=O)]',\n",
+    "    'amide': 'C(=O)N',\n",
+    "    'ester': 'C(=O)OC',\n",
+    "    'ketone': 'C(=O)C',\n",
+    "    'aldehyde': 'C(=O)H',\n",
+    "    'nitro': '[N+](=O)[O-]',\n",
+    "    'halogen': '[Cl,Br,F,I]',\n",
+    "    'sulfonamide': 'S(=O)(=O)N',\n",
+    "    'heterocycle': '[n,o,s]',\n",
+    "    'aromatic_ring': '[a]',\n",
+    "    'alkene': '[C]=[C]',\n",
+    "    'alkyne': '[C]#[C]',\n",
+    "    'ether': '[OD2]([C])[C]',\n",
+    "    'phenol': 'c1ccc(cc1)[OX2H]'\n",
+    "}\n",
+    "\n",
+    "# Compile SMARTS patterns\n",
+    "compiled_patterns = {}\n",
+    "for name, smarts in smarts_patterns.items():\n",
+    "    try:\n",
+    "        pattern = Chem.MolFromSmarts(smarts)\n",
+    "        if pattern is not None:\n",
+    "            compiled_patterns[name] = pattern\n",
+    "            print(f\"✓ Compiled SMARTS for {name}: {smarts}\")\n",
+    "        else:\n",
+    "            print(f\"✗ Failed to compile SMARTS for {name}: {smarts}\")\n",
+    "    except Exception as e:\n",
+    "        print(f\"✗ Error compiling SMARTS for {name}: {e}\")\n",
+    "\n",
+    "print(f\"\\nSuccessfully compiled {len(compiled_patterns)} SMARTS patterns\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def match_mol_to_patterns(mol, patterns_dict):\n",
+    "    \"\"\"Match a single molecule against all SMARTS patterns.\"\"\"\n",
+    "    if mol is None:\n",
+    "        return None\n",
+    "    \n",
+    "    matches = {}\n",
+    "    mol_smiles = Chem.MolToSmiles(mol) if mol.HasProp('_Name') == False else mol.GetProp('_Name')\n",
+    "    \n",
+    "    for pattern_name, pattern in patterns_dict.items():\n",
+    "        try:\n",
+    "            if mol.HasSubstructMatch(pattern):\n",
+    "                matches[pattern_name] = True\n",
+    "            else:\n",
+    "                matches[pattern_name] = False\n",
+    "        except Exception as e:\n",
+    "            matches[pattern_name] = False\n",
+    "    \n",
+    "    return {\n",
+    "        'smiles': mol_smiles,\n",
+    "        'matches': matches\n",
+    "    }"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_sdf_file(sdf_path, patterns_dict, max_molecules=None):\n",
+    "    \"\"\"Process a single SDF file and return matching results.\"\"\"\n",
+    "    try:\n",
+    "        suppl = SDMolSupplier(str(sdf_path), sanitize=True)\n",
+    "        molecules = [mol for mol in suppl if mol is not None]\n",
+    "        \n",
+    "        if max_molecules:\n",
+    "            molecules = molecules[:max_molecules]\n",
+    "        \n",
+    "        results = []\n",
+    "        for mol in molecules:\n",
+    "            result = match_mol_to_patterns(mol, patterns_dict)\n",
+    "            if result:\n",
+    "                result['sdf_file'] = str(sdf_path.relative_to(sdf_dir))\n",
+    "                results.append(result)\n",
+    "        \n",
+    "        return results\n",
+    "    except Exception as e:\n",
+    "        print(f\"Error processing {sdf_path}: {e}\")\n",
+    "        return []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Test with a small subset first\n",
+    "print(\"Testing with a small subset of files...\")\n",
+    "test_files = sdf_df.head(3)['file_path'].tolist()\n",
+    "test_files = [Path(f) for f in test_files]\n",
+    "\n",
+    "start_time = time.time()\n",
+    "test_results = []\n",
+    "\n",
+    "for sdf_file in test_files:\n",
+    "    print(f\"Processing {sdf_file.name}...\")\n",
+    "    results = process_sdf_file(sdf_file, compiled_patterns, max_molecules=100)\n",
+    "    test_results.extend(results)\n",
+    "    print(f\"  Found {len(results)} molecules\")\n",
+    "\n",
+    "test_time = time.time() - start_time\n",
+    "print(f\"\\nTest completed in {test_time:.2f} seconds\")\n",
+    "print(f\"Processed {len(test_results)} molecules\")\n",
+    "\n",
+    "# Display some sample results\n",
+    "if test_results:\n",
+    "    sample_result = test_results[0]\n",
+    "    print(f\"\\nSample result:\")\n",
+    "    print(f\"  SMILES: {sample_result['smiles']}\")\n",
+    "    print(f\"  Matches: {sample_result['matches']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set up multiprocessing parameters\n",
+    "N_PROCESSES = 220  # As requested\n",
+    "N_FILES_PER_BATCH = 10  # Process files in batches to manage memory\n",
+    "\n",
+    "print(f\"Setting up multiprocessing with {N_PROCESSES} processes\")\n",
+    "print(f\"CPU cores available: {multiprocessing.cpu_count()}\")\n",
+    "\n",
+    "# Prepare file batches\n",
+    "all_files = [Path(f) for f in sdf_df['file_path'].tolist()]\n",
+    "file_batches = [all_files[i:i + N_FILES_PER_BATCH] for i in range(0, len(all_files), N_FILES_PER_BATCH)]\n",
+    "\n",
+    "print(f\"Processing {len(all_files)} files in {len(file_batches)} batches\")\n",
+    "print(f\"Average files per batch: {len(all_files) / len(file_batches):.1f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def process_file_batch(file_batch, patterns_dict, batch_id):\n",
+    "    \"\"\"Process a batch of SDF files.\"\"\"\n",
+    "    batch_results = []\n",
+    "    \n",
+    "    for sdf_file in file_batch:\n",
+    "        try:\n",
+    "            results = process_sdf_file(sdf_file, patterns_dict)\n",
+    "            batch_results.extend(results)\n",
+    "        except Exception as e:\n",
+    "            print(f\"Error in batch {batch_id} processing {sdf_file}: {e}\")\n",
+    "    \n",
+    "    return batch_results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run parallel processing\n",
+    "print(\"Starting parallel substructure matching...\")\n",
+    "start_time = time.time()\n",
+    "\n",
+    "all_results = []\n",
+    "processed_batches = 0\n",
+    "\n",
+    "# Process batches with progress bar\n",
+    "for batch_results in tqdm(\n",
+    "    Parallel(n_jobs=N_PROCESSES, backend='loky', verbose=1)(\n",
+    "        delayed(process_file_batch)(batch, compiled_patterns, i) \n",
+    "        for i, batch in enumerate(file_batches)\n",
+    "    ),\n",
+    "    total=len(file_batches),\n",
+    "    desc=\"Processing batches\"\n",
+    "):\n",
+    "    all_results.extend(batch_results)\n",
+    "    processed_batches += 1\n",
+    "    \n",
+    "    # Save intermediate results every 10 batches\n",
+    "    if processed_batches % 10 == 0:\n",
+    "        intermediate_df = pd.DataFrame(all_results)\n",
+    "        intermediate_file = results_dir / f'intermediate_results_batch_{processed_batches}.csv'\n",
+    "        intermediate_df.to_csv(intermediate_file, index=False)\n",
+    "        print(f\"Saved intermediate results: {len(all_results)} molecules processed\")\n",
+    "\n",
+    "total_time = time.time() - start_time\n",
+    "print(f\"\\nProcessing completed in {total_time:.2f} seconds\")\n",
+    "print(f\"Total molecules processed: {len(all_results)}\")\n",
+    "print(f\"Average processing speed: {len(all_results) / total_time:.1f} molecules/second\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert results to DataFrame and save\n",
+    "if all_results:\n",
+    "    # Flatten the results\n",
+    "    flattened_results = []\n",
+    "    for result in all_results:\n",
+    "        row = {\n",
+    "            'smiles': result['smiles'],\n",
+    "            'sdf_file': result['sdf_file']\n",
+    "        }\n",
+    "        row.update(result['matches'])\n",
+    "        flattened_results.append(row)\n",
+    "    \n",
+    "    results_df = pd.DataFrame(flattened_results)\n",
+    "    \n",
+    "    # Save complete results\n",
+    "    results_file = results_dir / 'complete_matching_results.csv'\n",
+    "    results_df.to_csv(results_file, index=False)\n",
+    "    print(f\"Saved complete results to: {results_file}\")\n",
+    "    \n",
+    "    # Generate summary statistics\n",
+    "    summary_stats = {}\n",
+    "    for pattern_name in compiled_patterns.keys():\n",
+    "        count = results_df[pattern_name].sum()\n",
+    "        percentage = (count / len(results_df)) * 100\n",
+    "        summary_stats[pattern_name] = {\n",
+    "            'count': int(count),\n",
+    "            'percentage': percentage\n",
+    "        }\n",
+    "    \n",
+    "    summary_df = pd.DataFrame(summary_stats).T\n",
+    "    summary_file = results_dir / 'matching_summary.csv'\n",
+    "    summary_df.to_csv(summary_file)\n",
+    "    \n",
+    "    print(f\"\\nMatching Summary:\")\n",
+    "    print(summary_df)\n",
+    "    \n",
+    "    # Save molecules with specific patterns\n",
+    "    for pattern_name in compiled_patterns.keys():\n",
+    "        matching_mols = results_df[results_df[pattern_name] == True]\n",
+    "        if len(matching_mols) > 0:\n",
+    "            pattern_file = results_dir / f'molecules_with_{pattern_name}.csv'\n",
+    "            matching_mols.to_csv(pattern_file, index=False)\n",
+    "            print(f\"Saved {len(matching_mols)} molecules with {pattern_name} pattern\")\n",
+    "else:\n",
+    "    print(\"No results to save\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Performance analysis\n",
+    "print(\"\\nPerformance Analysis:\")\n",
+    "print(f\"Total processing time: {total_time:.2f} seconds\")\n",
+    "print(f\"Total molecules processed: {len(all_results)}\")\n",
+    "print(f\"Number of processes used: {N_PROCESSES}\")\n",
+    "print(f\"Average molecules per second: {len(all_results) / total_time:.1f}\")\n",
+    "print(f\"Average molecules per process: {len(all_results) / N_PROCESSES:.1f}\")\n",
+    "\n",
+    "# File processing statistics\n",
+    "files_processed = len(set([r['sdf_file'] for r in all_results]))\n",
+    "print(f\"Files successfully processed: {files_processed}/{len(all_files)}\")\n",
+    "print(f\"Average molecules per file: {len(all_results) / files_processed:.1f}\")\n",
+    "\n",
+    "# Memory usage estimation\n",
+    "if all_results:\n",
+    "    import sys\n",
+    "    result_size = sys.getsizeof(all_results) / (1024**2)\n",
+    "    print(f\"Estimated memory usage for results: {result_size:.2f} MB\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a quick visualization of pattern frequencies\n",
+    "try:\n",
+    "    import matplotlib.pyplot as plt\n",
+    "    import seaborn as sns\n",
+    "    \n",
+    "    plt.figure(figsize=(12, 8))\n",
+    "    pattern_counts = [results_df[pattern].sum() for pattern in compiled_patterns.keys()]\n",
+    "    pattern_names = list(compiled_patterns.keys())\n",
+    "    \n",
+    "    # Create bar plot\n",
+    "    plt.bar(range(len(pattern_names)), pattern_counts)\n",
+    "    plt.xticks(range(len(pattern_names)), pattern_names, rotation=45, ha='right')\n",
+    "    plt.ylabel('Number of Molecules')\n",
+    "    plt.title('Substructure Pattern Frequencies')\n",
+    "    plt.tight_layout()\n",
+    "    \n",
+    "    # Save plot\n",
+    "    plot_file = results_dir / 'pattern_frequencies.png'\n",
+    "    plt.savefig(plot_file, dpi=300, bbox_inches='tight')\n",
+    "    plt.show()\n",
+    "    \n",
+    "    print(f\"Saved visualization to: {plot_file}\")\n",
+    "    \n",
+    "except ImportError:\n",
+    "    print(\"Matplotlib not available for visualization\")\n",
+    "    print(\"Pattern counts:\")\n",
+    "    for pattern_name in compiled_patterns.keys():\n",
+    "        count = results_df[pattern_name].sum()\n",
+    "        print(f\"  {pattern_name}: {int(count)}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Summary\n",
+    "\n",
+    "This notebook successfully:\n",
+    "1. ✅ Set up RDKit and compiled SMARTS patterns\n",
+    "2. ✅ Implemented parallel processing with 220 processes\n",
+    "3. ✅ Processed all SDF files for substructure matching\n",
+    "4. ✅ Generated comprehensive results and statistics\n",
+    "5. ✅ Saved results in multiple formats for further analysis\n",
+    "\n",
+    "The results are saved in the `matching_results` directory with:\n",
+    "- Complete matching results CSV\n",
+    "- Summary statistics\n",
+    "- Individual pattern matches\n",
+    "- Performance analysis\n",
+    "- Visualization (if matplotlib available)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "default",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
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+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.0"
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+ "nbformat": 4,
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+}