install
source · Clone the upstream repo
git clone https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills-
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/mdbabumiamssm/LLMs-Universal-Life-Science-and-Clinical-Skills- "$T" && mkdir -p ~/.claude/skills && cp -r "$T/Skills/Drug_Discovery/Chemoinformatics/substructure-search" ~/.claude/skills/mdbabumiamssm-llms-universal-life-science-and-clinical-skills-substructure-searc && rm -rf "$T"
manifest:
Skills/Drug_Discovery/Chemoinformatics/substructure-search/SKILL.mdsource content
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# COPYRIGHT NOTICE
# This file is part of the "Universal Biomedical Skills" project.
# Copyright (c) 2026 MD BABU MIA, PhD <md.babu.mia@mssm.edu>
# All Rights Reserved.
#
# This code is proprietary and confidential.
# Unauthorized copying of this file, via any medium is strictly prohibited.
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name: bio-substructure-search description: Searches molecular libraries for substructure matches using SMARTS patterns with RDKit. Filters compounds by pharmacophore features, functional groups, or scaffold matches with atom mapping. Use when finding compounds containing specific chemical moieties or filtering libraries by structural features. tool_type: python primary_tool: RDKit measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes. allowed-tools:
- read_file
- run_shell_command
Substructure Search
Find molecules containing specific structural patterns using SMARTS.
Basic Substructure Search
from rdkit import Chem mol = Chem.MolFromSmiles('c1ccc(O)cc1CCO') # Check if pattern exists pattern = Chem.MolFromSmarts('[OH]') # Hydroxyl group has_hydroxyl = mol.HasSubstructMatch(pattern) print(f'Contains hydroxyl: {has_hydroxyl}') # Get all matches (atom indices) matches = mol.GetSubstructMatches(pattern) print(f'Hydroxyl positions: {matches}')
Common SMARTS Patterns
| Pattern | SMARTS | Description |
|---|---|---|
| Hydroxyl | | Alcohol/phenol |
| Primary amine | | Primary amine |
| Secondary amine | | Secondary amine |
| Carboxylic acid | | COOH |
| Amide | | C(=O)N |
| Benzene | | Phenyl ring |
| Any aromatic | | Any aromatic atom |
| Halogen | | Any halogen |
Library Filtering
from rdkit import Chem def filter_by_substructure(molecules, smarts, exclude=False): ''' Filter molecules by substructure presence/absence. Args: molecules: List of RDKit mol objects smarts: SMARTS pattern string exclude: If True, return molecules WITHOUT the pattern ''' pattern = Chem.MolFromSmarts(smarts) if pattern is None: raise ValueError(f'Invalid SMARTS: {smarts}') filtered = [] for mol in molecules: if mol is None: continue has_match = mol.HasSubstructMatch(pattern) if exclude: if not has_match: filtered.append(mol) else: if has_match: filtered.append(mol) return filtered # Filter for amines amines = filter_by_substructure(library, '[NX3;H2,H1,H0]') # Exclude reactive groups clean = filter_by_substructure(library, '[N+]([O-])=O', exclude=True) # No nitro
Multiple Pattern Filtering
def filter_multiple_patterns(molecules, include_patterns=None, exclude_patterns=None): ''' Filter by multiple inclusion and exclusion patterns. ''' result = list(molecules) if include_patterns: for smarts in include_patterns: pattern = Chem.MolFromSmarts(smarts) result = [m for m in result if m and m.HasSubstructMatch(pattern)] if exclude_patterns: for smarts in exclude_patterns: pattern = Chem.MolFromSmarts(smarts) result = [m for m in result if m and not m.HasSubstructMatch(pattern)] return result # Find compounds with both amine and carboxylic acid (amino acids) amino_acids = filter_multiple_patterns( library, include_patterns=['[NX3;H2]', '[CX3](=O)[OX2H1]'] )
Atom Mapping
from rdkit import Chem def get_substructure_atoms(mol, smarts): ''' Get all atoms matching a pattern with their indices. ''' pattern = Chem.MolFromSmarts(smarts) matches = mol.GetSubstructMatches(pattern) results = [] for match in matches: atoms = [mol.GetAtomWithIdx(i) for i in match] results.append({ 'indices': match, 'symbols': [a.GetSymbol() for a in atoms] }) return results # Find and characterize all aromatic rings mol = Chem.MolFromSmiles('c1ccc2c(c1)cccc2') rings = get_substructure_atoms(mol, 'c1ccccc1') print(f'Found {len(rings)} aromatic 6-membered rings')
Recursive SMARTS
# Recursive SMARTS for complex patterns # Phenyl attached to carbonyl pattern = '[$(c1ccccc1C(=O))]' # Ortho-substituted phenyl ortho_pattern = '[$(c1ccc([*])cc1[*])]' # Electron-withdrawing group on aromatic ewg_aromatic = '[$(c[$(C(=O)),$(C#N),$(N(=O)=O)])]' mol = Chem.MolFromSmiles('c1ccc(C(=O)O)cc1') pattern = Chem.MolFromSmarts('[$(c1ccccc1C(=O))]') print(mol.HasSubstructMatch(pattern)) # True
Visualization with Highlighting
from rdkit.Chem.Draw import rdMolDraw2D def draw_with_highlights(mol, smarts, filename): '''Draw molecule with substructure highlighted.''' pattern = Chem.MolFromSmarts(smarts) match = mol.GetSubstructMatch(pattern) if not match: print('No match found') return drawer = rdMolDraw2D.MolDraw2DCairo(400, 300) drawer.DrawMolecule(mol, highlightAtoms=match) drawer.FinishDrawing() with open(filename, 'wb') as f: f.write(drawer.GetDrawingText()) # Highlight carboxylic acid draw_with_highlights(mol, '[CX3](=O)[OX2H1]', 'highlighted.png')
Related Skills
- molecular-io - Load molecules for searching
- similarity-searching - Fingerprint-based searching
- admet-prediction - Filter before ADMET analysis