medchem

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

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