medchem

Medchem

Overview

Medchem is a Python library for molecular filtering and prioritization in drug discovery. It provides hundreds of established medicinal chemistry rules, structural alerts, and chemical group detectors to triage compound libraries at scale. All filters support parallel execution and return structured results.

When to Use

• Applying drug-likeness rules (Lipinski, Veber, Oprea, CNS, REOS) to compound libraries
• Filtering molecules by structural alerts (PAINS, NIBR, Lilly Demerits)
• Detecting specific chemical groups (hinge binders, Michael acceptors, reactive groups)
• Calculating molecular complexity metrics (Bertz, Whitlock, Barone)
• Applying custom property constraints (MW, LogP, TPSA, rotatable bonds)
• Composing complex multi-rule filter queries with Boolean logic
• For SMILES/SDF parsing, descriptors, and fingerprints use rdkit-cheminformatics
• For high-level molecular manipulation use datamol-cheminformatics

Prerequisites

pip install medchem datamol

Medchem depends on RDKit and datamol. All molecule inputs are RDKit Chem.Mol objects; use datamol.to_mol() to convert from SMILES.

Quick Start

import datamol as dm
import medchem as mc

# Convert SMILES to molecules
smiles_list = ["CC(=O)OC1=CC=CC=C1C(=O)O", "c1ccccc1N", "O=C(O)c1ccccc1"]
mols = [dm.to_mol(s) for s in smiles_list]

# Apply Rule of Five + structural alerts in one pass
rule_filter = mc.rules.RuleFilters(rule_list=["rule_of_five"])
alert_filter = mc.structural.CommonAlertsFilters()

rule_results = rule_filter(mols=mols, n_jobs=-1)
alert_results = alert_filter(mols=mols, n_jobs=-1)

print(f"Rule results: {rule_results}")
print(f"Alert results: {[r['has_alerts'] for r in alert_results]}")

Core API

1. Drug-Likeness Rules

Apply established medicinal chemistry rules via mc.rules. Individual rules return bool; RuleFilters applies multiple rules in batch.

import medchem as mc

# Single rule on a SMILES string
passes = mc.rules.basic_rules.rule_of_five("CC(=O)OC1=CC=CC=C1C(=O)O")
print(f"Passes Ro5: {passes}")  # True

# Available individual rules:
# rule_of_five, rule_of_three, rule_of_oprea, rule_of_cns,
# rule_of_leadlike_soft, rule_of_leadlike_strict, rule_of_veber,
# rule_of_reos, rule_of_drug, golden_triangle, pains_filter

import datamol as dm
import medchem as mc

# Batch application with RuleFilters
mols = [dm.to_mol(s) for s in smiles_list]
rfilter = mc.rules.RuleFilters(
    rule_list=["rule_of_five", "rule_of_oprea", "rule_of_cns"]
)
results = rfilter(mols=mols, n_jobs=-1, progress=True)
# Returns list of dicts: [{"rule_of_five": True, "rule_of_oprea": False, ...}, ...]
print(f"First molecule: {results[0]}")

2. Structural Alert Filters

Detect problematic structural patterns via mc.structural. Three filter sets cover different scope and stringency.

import datamol as dm
import medchem as mc

mol = dm.to_mol("c1ccc(N)cc1")
mols = [dm.to_mol(s) for s in smiles_list]

# Common Alerts — general structural alerts from ChEMBL / literature
alert_filter = mc.structural.CommonAlertsFilters()
has_alerts, details = alert_filter.check_mol(mol)  # single molecule
batch_results = alert_filter(mols=mols, n_jobs=-1, progress=True)
# Each result: {"has_alerts": bool, "alert_details": [...], "num_alerts": int}
print(f"Alerts: {batch_results[0]}")

import medchem as mc

# NIBR Filters — Novartis industrial filter set (returns bool list)
nibr_filter = mc.structural.NIBRFilters()
nibr_results = nibr_filter(mols=mols, n_jobs=-1)
print(f"NIBR pass: {nibr_results}")  # [True, False, ...]

# Lilly Demerits — 275 patterns, molecules rejected at >100 demerits
lilly_filter = mc.structural.LillyDemeritsFilters()
lilly_results = lilly_filter(mols=mols, n_jobs=-1)
# Each result: {"demerits": int, "passes": bool, "matched_patterns": [...]}
print(f"Lilly: {lilly_results[0]}")

3. Chemical Groups

Detect specific functional group motifs via mc.groups.ChemicalGroup.

Predefined groups: hinge_binders, phosphate_binders, michael_acceptors, reactive_groups.

import medchem as mc

# Check for kinase hinge binders and Michael acceptors
group = mc.groups.ChemicalGroup(
    groups=["hinge_binders", "michael_acceptors"]
)

has_matches = group.has_match(mols)        # List[bool]
match_info = group.get_matches(mols[0])    # {group_name: [(atom_indices), ...]}
all_matches = group.get_all_matches(mols)  # List[Dict]
print(f"Has hinge binder: {has_matches}")

# Custom SMARTS patterns
custom = mc.groups.ChemicalGroup(
    groups=["reactive_groups"],
    custom_smarts={"trifluoromethyl_ketone": "[C;H0](=O)C(F)(F)F"}
)

4. Named Catalogs

Access curated chemical structure catalogs via mc.catalogs.

Available catalogs: functional_groups, protecting_groups, reagents, fragments.

import medchem as mc

catalog = mc.catalogs.NamedCatalogs.get("functional_groups")
matches = catalog.get_matches(mol)
print(f"Functional group matches: {matches}")

5. Molecular Complexity

Calculate synthetic accessibility proxies via mc.complexity.

Methods: bertz (topological), whitlock, barone.

import datamol as dm
import medchem as mc

mol = dm.to_mol("CC(=O)OC1=CC=CC=C1C(=O)O")

# Single molecule complexity
score = mc.complexity.calculate_complexity(mol, method="bertz")
print(f"Bertz complexity: {score:.1f}")

# Batch filtering by complexity threshold
cfilter = mc.complexity.ComplexityFilter(max_complexity=500, method="bertz")
results = cfilter(mols=mols, n_jobs=-1)
print(f"Passes complexity: {results}")  # List[bool]

6. Property Constraints

Apply custom property-based constraints via mc.constraints.Constraints.

import medchem as mc

constraints = mc.constraints.Constraints(
    mw_range=(200, 500),
    logp_range=(-2, 5),
    tpsa_max=140,
    rotatable_bonds_max=10,
    hbd_max=5,
    hba_max=10,
    rings_range=(1, 5),
    aromatic_rings_max=3,
)
results = constraints(mols=mols, n_jobs=-1)
# Each result: {"passes": bool, "violations": ["mw_range", ...]}
print(f"Violations: {results[0]}")

7. Query Language

Compose complex filter logic with Boolean expressions via mc.query.

import medchem as mc

# Parse a query combining rules, alerts, and property checks
query = mc.query.parse("rule_of_five AND NOT common_alerts")
results = query.apply(mols=mols, n_jobs=-1)  # List[bool]
print(f"Passing: {sum(results)}/{len(results)}")

# More complex queries
q2 = mc.query.parse("rule_of_cns AND complexity < 400")
q3 = mc.query.parse("(rule_of_five OR rule_of_oprea) AND NOT pains_filter")
q4 = mc.query.parse("mw > 200 AND mw < 500 AND logp < 5")

8. Functional API & Utilities

Shortcut functions in mc.functional and utilities in mc.utils.

import medchem as mc

# Functional API — one-liner filters
nibr_ok = mc.functional.nibr_filter(mols=mols, n_jobs=-1)    # List[bool]
alerts = mc.functional.common_alerts_filter(mols=mols, n_jobs=-1)
lilly = mc.functional.lilly_demerits_filter(mols=mols, n_jobs=-1)

# Utilities
standardized = mc.utils.standardize_mol(mol)  # sanitize, neutralize charges
batch_out = mc.utils.batch_process(
    mols=mols, func=mc.complexity.calculate_complexity,
    n_jobs=-1, progress=True, batch_size=100
)

Key Concepts

Rule Thresholds Quick Reference

| Rule | MW | LogP | HBD | HBA | RotBonds | TPSA | Other | |------|----|----- |-----|-----|----------|------|-------| | Ro5 (Lipinski) | ≤500 | ≤5 | ≤5 | ≤10 | — | — | — | | Veber | — | — | — | — | ≤10 | ≤140 | — | | Oprea (lead) | 200-350 | -2 to 4 | — | — | ≤7 | — | Rings ≤4 | | Leadlike Soft | 250-450 | -3 to 4 | — | — | ≤10 | — | — | | Leadlike Strict | 200-350 | -2 to 3.5 | — | — | ≤7 | — | Rings 1-3 | | CNS | ≤450 | -1 to 5 | ≤2 | — | — | ≤90 | — | | REOS | 200-500 | -5 to 5 | 0-5 | 0-10 | — | — | — | | Ro3 (fragment) | ≤300 | ≤3 | ≤3 | ≤3 | ≤3 | ≤60 | — | | Golden Triangle | 200-5