Medchem

Overview

Medchem is a Python library from datamol-io for molecular filtering and prioritization in drug discovery. Apply literature-derived drug-likeness rules, named alert catalogs, complexity thresholds, chemical-group detection, and a custom query language to triage compound libraries at scale. Filters are context-specific guidelines — combine with domain expertise and target knowledge.

Verified runtime: medchem 2.1.1, datamol 0.13.0, RDKit 2026.3.6; Python 3.11+. RuleFilters and structural classes return pandas DataFrames. Filters run locally; installation and documentation lookup need network access. Lilly native execution was not tested in this review.

When to Use This Skill

This skill should be used when:

• Applying drug-likeness rules (Lipinski, Veber, CNS, lead-like) to compound libraries
• Filtering molecules by structural alerts, PAINS, or NIBR screening-deck rules
• Prioritizing compounds for hit-to-lead or lead optimization
• Calculating complexity metrics against ZINC-derived thresholds
• Detecting functional groups or named substructure catalogs
• Building multi-criteria filters with the medchem query language

Installation

uv pip install "medchem==2.1.1" "datamol==0.13.0" "rdkit==2026.3.6"

Optional Lilly integration: install the upstream checksum-pinned native tools beside the active Python. This downloads source, builds executables, and runs native regression tests (C++ compiler, make, zlib, Ruby; WSL on Windows). This installation command is documented upstream, not executed here:

medchem install-lilly

Core Capabilities

1. Medicinal Chemistry Rules

Apply established drug-likeness rules via medchem.rules.

List available rules:

import medchem as mc

mc.rules.RuleFilters.list_available_rules_names()
# ['rule_of_five', 'rule_of_five_beyond', 'rule_of_four', 'rule_of_three', ...]

Single rule on one molecule:

import datamol as dm
import medchem as mc

smiles = "CC(=O)OC1=CC=CC=C1C(=O)O"  # aspirin
mc.rules.basic_rules.rule_of_five(smiles)   # True
mc.rules.basic_rules.rule_of_cns(smiles)    # True
mc.rules.basic_rules.rule_of_veber(smiles)  # True

Multiple rules with RuleFilters (returns a DataFrame):

import datamol as dm
import medchem as mc

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", "rule_of_leadlike_soft"]
)
df = rfilter(mols=mols, n_jobs=-1, progress=True, keep_props=False)

# Columns: mol, pass_all, pass_any, rule_of_five, rule_of_oprea, ...
passing = df[df["pass_all"]]

Examples below use already parsed mol_list, smiles_list, and candidates supplied by the caller; reject missing/empty structures before filtering. Use keep_props=True to include computed descriptors (mw, clogp, tpsa, etc.) in the result.

2. Structural Alert Filters

Detect problematic patterns with medchem.structural. Both classes return DataFrames with pass_filter, status, and reasons columns.

Common alerts (ChEMBL-derived rule sets):

import medchem as mc

alert_filter = mc.structural.CommonAlertsFilters(alerts_set=["BMS", "Dundee", "Glaxo"])
df = alert_filter(mols=mol_list, n_jobs=-1, progress=True)
# df columns: mol, pass_filter, status, reasons

clean = df[df["pass_filter"]]

NIBR filters (Novartis screening-deck curation):

nibr_filter = mc.structural.NIBRFilters()
df = nibr_filter(mols=mol_list, n_jobs=-1, progress=True)
# df columns: mol, pass_filter, status, severity, reasons, n_covalent_motif, special_mol

The class rejects explicit exclusion alerts. To also reject accumulated flag severity ≥10, use df["pass_filter"] & (df["severity"] < 10), or mc.functional.nibr_filter(..., max_severity=10). The functional cutoff is strict < 10 and assumes valid molecules: it checks severity alone and can admit parse failures with severity zero. Prevalidate inputs.

3. Named Catalog Filters (PAINS, Brenk, etc.)

Use medchem.catalogs.NamedCatalogs for RDKit FilterCatalog instances, or the functional API:

import medchem as mc

# List available named catalogs
mc.catalogs.list_named_catalogs()
# ['tox', 'pains', 'pains_a', 'brenk', 'nibr', 'zinc', ...]

# Functional API — True means molecule passes (no alert match)

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