claude-scientific-skills/scientific-packages/medchem/SKILL.md

name, description

name

description

medchem

Python library for medicinal chemistry filtering and compound prioritization in drug discovery workflows. Use medchem when you need to: apply drug-likeness rules (Lipinski Rule of Five, CNS rules, leadlike criteria, Veber rules, Oprea rules), detect structural alerts and problematic substructures (PAINS filters, NIBR alerts, Lilly demerits, common structural alerts), filter compound libraries by medicinal chemistry criteria, calculate molecular complexity metrics (Bertz, Whitlock, Barone), identify specific chemical groups (hinge binders, phosphate binders, Michael acceptors), apply property-based constraints (molecular weight, LogP, TPSA, rotatable bonds), screen large compound collections for drug-like properties, prioritize hits from virtual screening, optimize lead compounds during medicinal chemistry campaigns, validate compound libraries before biological testing, or perform batch processing of molecular datasets. Medchem integrates with RDKit and datamol, accepts SMILES strings and RDKit mol objects, provides parallel processing for large datasets, includes a query language for complex filtering criteria, and offers both functional and object-oriented APIs. Essential for computational medicinal chemistry, compound library management, hit-to-lead optimization, and drug discovery pipeline workflows.

Medchem

Overview

Medchem is a Python library for molecular filtering and prioritization in drug discovery workflows. It provides hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale.

Key Principle: Rules and filters are always context-specific. Avoid blindly applying filters—marketed drugs often don't pass standard medchem filters, and prodrugs may intentionally violate rules. Use these tools as guidelines combined with domain expertise.

Installation

Install medchem via conda or pip:

# Via conda
micromamba install -c conda-forge medchem

# Via pip
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:

1. Common Alerts - General structural alerts derived from ChEMBL curation and literature
2. NIBR Filters - Novartis Institutes for BioMedical Research filter set
3. 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

1. Context Matters: Don't blindly apply filters. Understand the biological target and chemical space.

2. Combine Multiple Filters: Use rules, structural alerts, and domain knowledge together for better decisions.

3. Use Parallelization: For large datasets (>1000 molecules), always use n_jobs=-1 for parallel processing.

4. Iterative Refinement: Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed.

5. Document Filtering Decisions: Track which molecules were filtered out and why for reproducibility.

6. Validate Results: Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules.

7. 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