Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.
npxskills add davila7/claude-code-templates--skill medchemLoading…
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.
This skill should be used when:
uv pip install medchem
Apply established drug-likeness rules to molecules using the medchem.rules module.
Available Rules:
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.
Detect potentially problematic structural patterns using the medchem.structural module.
Available Filters:
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)
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
)
Identify specific chemical groups and functional groups using medchem.groups.
Available Groups:
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)
Access curated collections of chemical structures through medchem.catalogs.
Available Catalogs:
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)
Calculate complexity metrics that approximate synthetic accessibility using medchem.complexity.
Common Metrics:
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)
Apply custom property-based constraints using medchem.constraints.
Example Constraints:
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)
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)
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)
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())
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]
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.
Comprehensive API reference covering all medchem modules with detailed function signatures, parameters, and return types.
Complete catalog of available rules, filters, and alerts with descriptions, thresholds, and literature references.
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
Official documentation: https://medchem-docs.datamol.io/ GitHub repository: https://github.com/datamol-io/medchem
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