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.

Checked against: medchem 2.0.5 (PyPI stable, released 2024-11-18; still the current release as of August 2026) — 22 rules and 32 named catalogs verified live. Requires Python ≥3.9. Depends on datamol and RDKit (installed automatically). RuleFilters and structural filter classes return pandas DataFrames. Lilly demerits require optional native binaries (mamba install lilly-medchem-rules).

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 datamol

Optional — Eli Lilly demerit filter (requires conda-forge native binaries):

mamba install -c conda-forge lilly-medchem-rules

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"]]

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()
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

Compounds with severity >= 10 are excluded by default (see NIBR paper).

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)
passes = mc.functional.alert_filter(mols=mol_list, alerts=["pains"], n_jobs=-1)

# Or via catalog objects
passes = mc.functional.catalog_filter(
    mols=mol_list,
    catalogs=[mc.catalogs.NamedCatalogs.pains()],
    n_jobs=-1,
)

4. Functional API

medchem.functional provides one-call wrappers that return boolean masks (True = passes):

import medchem as mc

mc.functional.rules_filter(mols=mol_list, rules=["rule_of_five", "rule_of_cns"], n_jobs=-1)
mc.functional.nibr_filter(mols=mol_list, max_severity=10, n_jobs=-1)
mc.functional.alert_filter(mols=mol_list, alerts=["pains", "brenk"], n_jobs=-1)
mc.functional.complexity_filter(mols=mol_list, complexity_metric="bertz", limit="99", n_jobs=-1)

Other helpers: catalog_filter, chemical_group_filter, lilly_demerit_filter (requires optional binaries), macrocycle_filter, bredt_filter, protecting_groups_filter, and more.

5. Chemical Groups

Detect functional groups and curated pattern collections via medchem.groups:

import medchem as mc

# Browse available group collections
mc.groups.list_default_chemical_groups()
# ['privileged_scaffolds', 'common_warhead_covalent_inhibitors', 'rings_in_drugs', ...]

group = mc.groups.ChemicalGroup(groups=["privileged_scaffolds"])
group.has_match(mol)                          # bool
group.get_matches(mol)                        # dict of group → atom indices
group.filter(mols)                            # molecules matching the group

# Returns molecules that do NOT match the group
mc.functional.chemical_group_filter(mols=mol_list, chemical_group=group, n_jobs=-1)

Custom groups can be loaded from a file via groups_db (CSV with smiles/smarts, name, group columns).

6. Molecular Complexity

Compare complexity metrics to precomputed ZINC-15 percentile thresholds:

import medchem as mc

# Single molecule
cf = mc.complexity.ComplexityFilter(limit="99", complexity_metric="bertz")
cf(mol)  # True if below 99th-percentile threshold

# Batch via functional API
mc.functional.complexity_filter(
    mols=mol_list,
    complexity_metric="bertz",  # also: sas, qed, whitlock, barone, smcm, twc
    limit="99",
    n_jobs=-1,
)

# Direct metric functions
mc.complexity.WhitlockCT(mol)
mc.complexity.BaroneCT(mol)

7. Scaffold Constraints

medchem.constraints.Constraints matches a core scaffold and applies per-atom constraint functions — not simple MW/LogP ranges. For property bounds, use RuleFilters, descriptors via mc.rules.list_descriptors(), or the query language.

import datamol as dm
import medchem as mc

core = dm.to_mol("c1ccccc1")
constraints = mc.constraints.Constraints(
    core=core,
    constraint_fns={"query": lambda mol, atom_idx, query: ...},
)
constraints(mol)

8. Medchem Query Language

Build multi-criteria filters with medchem.query.QueryFilter:

import medchem as mc

# Rule + alert combination
qf = mc.query.QueryFilter('MATCHRULE("rule_of_five") AND NOT HASALERT("pains")')
mask = qf(mols=mol_list, n_jobs=-1)  # list[bool]

# CNS-like with property bounds
qf = mc.query.QueryFilter('MATCHRULE("rule_of_cns") AND HASPROP("tpsa", <=, 90)')
mask = qf(mols=mol_list, n_jobs=-1)

Query syntax:

• MATCHRULE("rule_of_five") — apply a named rule
• HASALERT("pains") — match a named catalog (pains, brenk, nibr, tox, …)
• HASPROP("mw", <, 500) — compare a descriptor (unquoted comparator)
• HASGROUP("privileged_scaffolds") — match a chemical group
• HASSUBSTRUCTURE("c1ccccc1") — substructure match
• Operators: AND, OR, NOT

List available descriptors: mc.rules.list_descriptors()

Workflow Patterns

Pattern 1: Initial Triage of a Compound Library

import datamol as dm
import medchem as mc
import pandas as pd

df = pd.read_csv("compounds.csv")
mols = [dm.to_mol(s) for s in df["smiles"]]

# Drug-likeness rules
rules_df = mc.rules.RuleFilters(rule_list=["rule_of_five", "rule_of_veber"])(mols=mols, n_jobs=-1)

# PAINS + common alerts via query
qf = mc.query.QueryFilter('MATCHRULE("rule_of_five") AND NOT HASALERT("pains")')
pass_mask = qf(mols=mols, n_jobs=-1)

df["passes_rules"] = rules_df["pass_all"].values
df["drug_like"] = pass_mask
filtered_df = df[df["drug_like"]]
filtered_df.to_csv("filtered_compounds.csv", index=False)

Pattern 2: Lead Optimization Filtering

import medchem as mc

rules_df = mc.rules.RuleFilters(rule_list=["rule_of_leadlike_soft"])(mols=candidates, n_jobs=-1)
nibr_df = mc.structural.NIBRFilters()(mols=candidates, n_jobs=-1)
complex_mask = mc.functional.complexity_filter(
    mols=candidates, complexity_metric="bertz", limit="95", n_jobs=-1
)

passes = (
    rules_df["pass_all"]
    & nibr_df["pass_filter"]
    & complex_mask
)

Pattern 3: Detect Functional Groups

import medchem as mc

group = mc.groups.ChemicalGroup(groups=["common_warhead_covalent_inhibitors"])
matches = [group.has_match(mol) for mol in mol_list]
warhead_mols = [mol for mol, m in zip(mol_list, matches) if m]

Best Practices

1. Context matters — marketed drugs often violate Ro5; prodrugs and natural products are common exceptions.
2. Combine filters — rules, alert catalogs, and complexity thresholds work best together.
3. Use parallelization — pass n_jobs=-1 for libraries >1000 molecules.
4. Check return types — RuleFilters and structural classes return DataFrames; functional helpers return boolean arrays.
5. Lilly demerits are optional — install lilly-medchem-rules separately; default max demerits is 160 in the functional API.
6. Document decisions — retain status, reasons, and severity columns for audit trails.

Resources

references/api_guide.md

Module-by-module API reference with signatures, return types, and patterns.

references/rules_catalog.md

Catalog of available rules, alert sets, complexity metrics, and filter selection guidelines.

scripts/filter_molecules.py

Batch filtering script for CSV/TSV/SDF/SMILES inputs with configurable rules, alerts, and complexity thresholds.

python skills/medchem/scripts/filter_molecules.py input.csv \
  --rules rule_of_five,rule_of_cns --pains --nibr --output filtered.csv

--output is required. Alongside it the script writes <output>_summary.txt unless --no-summary is passed.

Composing with the rest of the bundle

• rdkit / datamol → before: parse and standardize first. An alert catalog matched against an unstandardized salt or tautomer answers a question about the wrong species.
• chembl → before: the measured library worth triaging.
• chemical-space → around: this is the cheap filter stage in an ultra-large screening cascade. Run it before docking, not after.
• admet-prediction → after: alerts cost nothing and catch much of what ADMET models flag more expensively and less reliably.
• generative-design / retrosynthesis → after: generated molecules need triage before route search. A PAINS hit is not worth a synthesis plan.
• autodock-vina / diffdock → before: docking 20,000 frequent hitters wastes the compute and pollutes the hit list.

Do not over-filter early. Alerts are context-specific guidelines, not verdicts — marketed drugs violate Ro5 routinely, and covalent warheads are a liability only if you did not want a covalent inhibitor. Keep status, reasons, and severity so a rejection can be argued with.

Documentation

• Official docs: https://medchem-docs.datamol.io/
• GitHub: https://github.com/datamol-io/medchem
• PyPI: https://pypi.org/project/medchem/ (2.0.5)