medchem

name medchem description Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. Medchem Overview Medchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise. When to Use This Skill This skill should be used when: Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries Filtering molecules by structural alerts or PAINS patterns Prioritizing compounds for lead optimization Assessing compound quality and medicinal chemistry properties Detecting reactive or problematic functional groups Calculating molecular complexity metrics Installation uv pip install medchem Core Capabilities 1. Medicinal Chemistry Rules Apply established drug-likeness rules to molecules using the medchem.rules module. Available Rules: Rule of Five (Lipinski) Rule of Oprea Rule of CNS Rule of leadlike (soft and strict) Rule of three Rule of Reos Rule of drug Rule of Veber Golden triangle PAINS filters Single Rule Application: import medchem as mc # Apply Rule of Five to a SMILES string smiles = "CC(=O)OC1=CC=CC=C1C(=O)O" # Aspirin passes = mc.rules.basic_rules.rule_of_five(smiles) # Returns: True # Check specific rules passes_oprea = mc.rules.basic_rules.rule_of_oprea(smiles) passes_cns = mc.rules.basic_rules.rule_of_cns(smiles) Multiple Rules with RuleFilters: import datamol as dm import medchem as mc # Load molecules mols = [dm.to_mol(smiles) for smiles in smiles_list] # Create filter with multiple rules rfilter = mc.rules.RuleFilters( rule_list=[ "rule_of_five" , "rule_of_oprea" , "rule_of_cns" , "rule_of_leadlike_soft" ] ) # Apply filters with parallelization results = rfilter( mols=mols, n_jobs=- 1 , # Use all CPU cores progress= True ) Result Format: Results are returned as dictionaries with pass/fail status and detailed information for each rule. 2. Structural Alert Filters Detect potentially problematic structural patterns using the medchem.structural module. Available Filters: Common Alerts - General structural alerts derived from ChEMBL curation and literature NIBR Filters - Novartis Institutes for BioMedical Research filter set Lilly Demerits - Eli Lilly's demerit-based system (275 rules, molecules rejected at >100 demerits) Common Alerts: import medchem as mc # Create filter alert_filter = mc.structural.CommonAlertsFilters() # Check single molecule mol = dm.to_mol( "c1ccccc1" ) has_alerts, details = alert_filter.check_mol(mol) # Batch filtering with parallelization results = alert_filter( mols=mol_list, n_jobs=- 1 , progress= True ) NIBR Filters: import medchem as mc # Apply NIBR filters nibr_filter = mc.structural.NIBRFilters() results = nibr_filter(mols=mol_list, n_jobs=- 1 ) Lilly Demerits: import medchem as mc # Calculate Lilly demerits lilly = mc.structural.LillyDemeritsFilters() results = lilly(mols=mol_list, n_jobs=- 1 ) # Each result includes demerit score and whether it passes (≤100 demerits) 3. Functional API for High-Level Operations The medchem.functional module provides convenient functions for common workflows. Quick Filtering: import medchem as mc # Apply NIBR filters to a list filter_ok = mc.functional.nibr_filter( mols=mol_list, n_jobs=- 1 ) # Apply common alerts alert_results = mc.functional.common_alerts_filter( mols=mol_list, n_jobs=- 1 ) 4. Chemical Groups Detection Identify specific chemical groups and functional groups using medchem.groups . Available Groups: Hinge binders Phosphate binders Michael acceptors Reactive groups Custom SMARTS patterns Usage: import medchem as mc # Create group detector group = mc.groups.ChemicalGroup(groups=[ "hinge_binders" ]) # Check for matches has_matches = group.has_match(mol_list) # Get detailed match information matches = group.get_matches(mol) 5. Named Catalogs Access curated collections of chemical structures through medchem.catalogs . Available Catalogs: Functional groups Protecting groups Common reagents Standard fragments Usage: import medchem as mc # Access named catalogs catalogs = mc.catalogs.NamedCatalogs # Use catalog for matching catalog = catalogs.get( "functional_groups" ) matches = catalog.get_matches(mol) 6. Molecular Complexity Calculate complexity metrics that approximate synthetic accessibility using medchem.complexity . Common Metrics: Bertz complexity Whitlock complexity Barone complexity Usage: import medchem as mc # Calculate complexity complexity_score = mc.complexity.calculate_complexity(mol) # Filter by complexity threshold complex_filter = mc.complexity.ComplexityFilter(max_complexity= 500 ) results = complex_filter(mols=mol_list) 7. Constraints Filtering Apply custom property-based constraints using medchem.constraints . Example Constraints: Molecular weight ranges LogP bounds TPSA limits Rotatable bond counts Usage: import medchem as mc # Define constraints constraints = mc.constraints.Constraints( mw_range=( 200 , 500 ), logp_range=(- 2 , 5 ), tpsa_max= 140 , rotatable_bonds_max= 10 ) # Apply constraints results = constraints(mols=mol_list, n_jobs=- 1 ) 8. Medchem Query Language Use a specialized query language for complex filtering criteria. Query Examples: # Molecules passing Ro5 AND not having common alerts "rule_of_five AND NOT common_alerts" # CNS-like molecules with low complexity "rule_of_cns AND complexity < 400" # Leadlike molecules without Lilly demerits "rule_of_leadlike AND lilly_demerits == 0" Usage: import medchem as mc # Parse and apply query query = mc.query.parse( "rule_of_five AND NOT common_alerts" ) results = query.apply(mols=mol_list, n_jobs=- 1 ) Workflow Patterns Pattern 1: Initial Triage of Compound Library Filter a large compound collection to identify drug-like candidates. import datamol as dm import medchem as mc import pandas as pd # Load compound library df = pd.read_csv( "compounds.csv" ) mols = [dm.to_mol(smi) for smi in df[ "smiles" ]] # Apply primary filters rule_filter = mc.rules.RuleFilters(rule_list=[ "rule_of_five" , "rule_of_veber" ]) rule_results = rule_filter(mols=mols, n_jobs=- 1 , progress= True ) # Apply structural alerts alert_filter = mc.structural.CommonAlertsFilters() alert_results = alert_filter(mols=mols, n_jobs=- 1 , progress= True ) # Combine results df[ "passes_rules" ] = rule_results[ "pass" ] df[ "has_alerts" ] = alert_results[ "has_alerts" ] df[ "drug_like" ] = df[ "passes_rules" ] & ~df[ "has_alerts" ] # Save filtered compounds filtered_df = df[df[ "drug_like" ]] filtered_df.to_csv( "filtered_compounds.csv" , index= False ) Pattern 2: Lead Optimization Filtering Apply stricter criteria during lead optimization. import medchem as mc # Create comprehensive filter filters = { "rules" : mc.rules.RuleFilters(rule_list=[ "rule_of_leadlike_strict" ]), "alerts" : mc.structural.NIBRFilters(), "lilly" : mc.structural.LillyDemeritsFilters(), "complexity" : mc.complexity.ComplexityFilter(max_complexity= 400 ) } # Apply all filters results = {} for name, filt in filters.items(): results[name] = filt(mols=candidate_mols, n_jobs=- 1 ) # Identify compounds passing all filters passes_all = all (r[ "pass" ] for r in results.values()) Pattern 3: Identify Specific Chemical Groups Find molecules containing specific functional groups or scaffolds. import medchem as mc # Create group detector for multiple groups group_detector = mc.groups.ChemicalGroup( groups=[ "hinge_binders" , "phosphate_binders" ] ) # Screen library matches = group_detector.get_all_matches(mol_list) # Filter molecules with desired groups mol_with_groups = [mol for mol, match in zip (mol_list, matches) if match ] Best Practices Context Matters : Don't blindly apply filters. Understand the biological target and chemical space. Combine Multiple Filters : Use rules, structural alerts, and domain knowledge together for better decisions. Use Parallelization : For large datasets (>1000 molecules), always use n_jobs=-1 for parallel processing. Iterative Refinement : Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed. Document Filtering Decisions : Track which molecules were filtered out and why for reproducibility. Validate Results : Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules. Consider Prodrugs : Molecules designed as prodrugs may intentionally violate standard medicinal chemistry rules. Resources references/api_guide.md Comprehensive API reference covering all medchem modules with detailed function signatures, parameters, and return types. references/rules_catalog.md Complete catalog of available rules, filters, and alerts with descriptions, thresholds, and literature references. scripts/filter_molecules.py Production-ready script for batch filtering workflows. Supports multiple input formats (CSV, SDF, SMILES), configurable filter combinations, and detailed reporting. Usage: python scripts/filter_molecules.py input.csv --rules rule_of_five,rule_of_cns --alerts nibr --output filtered.csv Documentation Official documentation: https://medchem-docs.datamol.io/ GitHub repository: https://github.com/datamol-io/medchem