Catalog of medicinal chemistry rules, alert sets, and filters in medchem 2.0.5.
Reference: Lipinski et al., Adv Drug Deliv Rev (1997) 23:3–25
Criteria: MW ≤ 500, LogP ≤ 5, HBD ≤ 5, HBA ≤ 10
mc.rules.basic_rules.rule_of_five(mol)
# or
mc.rules.RuleFilters(rule_list=["rule_of_five"])
Reference: Doak et al., (2015) — compounds beyond Ro5 for large binding sites
Criteria: MW ≤ 1000, LogP ∈ [-2, 10], HBD ≤ 6, HBA ≤ 15, TPSA ≤ 250, rotatable bonds ≤ 20
mc.rules.basic_rules.rule_of_five_beyond(mol)
Reference: Veber et al., J Med Chem (2002) 45:2615–2623
Criteria: Rotatable bonds ≤ 10, TPSA ≤ 140 Ų
mc.rules.basic_rules.rule_of_veber(mol)
Reference: Walters & Murcko, Adv Drug Deliv Rev (2002) 54:255–271
Criteria: MW 200–500, LogP −5 to 5, HBD 0–5, HBA 0–10
mc.rules.basic_rules.rule_of_reos(mol)
Additional literature filters available as rule_of_egan, rule_of_ghose, rule_of_pfizer_3_75, rule_of_gsk_4_400, rule_of_xu.
Combined soft drug-likeness criteria:
mc.rules.basic_rules.rule_of_druglike_soft(mol)
Reference: Oprea et al., J Chem Inf Comput Sci (2001) 41:1308–1315
Criteria: MW 200–350, LogP −2 to 4, rotatable bonds ≤ 7, rings ≤ 4
mc.rules.basic_rules.rule_of_oprea(mol)
Criteria: MW 250–450, LogP −3 to 4, rotatable bonds ≤ 10
mc.rules.basic_rules.rule_of_leadlike_soft(mol)
Reference: Congreve et al., Drug Discov Today (2003) 8:876–877
Criteria: MW ≤ 300, LogP ≤ 3, HBD ≤ 3, HBA ≤ 3, rotatable bonds ≤ 3, PSA ≤ 60 Ų
mc.rules.basic_rules.rule_of_three(mol)
Also available: rule_of_three_extended, rule_of_two, rule_of_four.
Criteria: MW ≤ 450, LogP −1 to 5, HBD ≤ 2, TPSA ≤ 90 Ų
mc.rules.basic_rules.rule_of_cns(mol)
Target-class filter for respiratory drugs:
mc.rules.basic_rules.rule_of_respiratory(mol)
For ML-generated molecules:
mc.rules.basic_rules.rule_of_generative_design(mol)
mc.rules.basic_rules.rule_of_generative_design_strict(mol)
Reference: Baell & Holloway, J Med Chem (2010) 53:2719–2740
Apply via named catalog — not a basic_rules function:
mc.functional.alert_filter(mols, alerts=["pains"], n_jobs=-1)
# or query: NOT HASALERT("pains")
Sub-catalogs: pains_a, pains_b, pains_c.
ChEMBL-curated rule sets (Glaxo, Dundee, BMS, MLSMR, etc.):
alert_filter = mc.structural.CommonAlertsFilters()
df = alert_filter(mols=mol_list, n_jobs=-1)
# status: exclude | flag | annotations | ok
Novartis screening-deck curation (Schuffenhauer et al., 2020):
nibr_filter = mc.structural.NIBRFilters()
df = nibr_filter(mols=mol_list, n_jobs=-1)
# severity >= 10 → excluded by default
Or via functional API with max_severity=10.
Requires mamba install lilly-medchem-rules. 275 structural patterns; default exclusion at >160 demerits:
mc.functional.lilly_demerit_filter(mols, max_demerits=160, n_jobs=-1)
Available via mc.catalogs.list_named_catalogs() and NamedCatalogs static methods:
| Catalog | Purpose |
|---|---|
pains, pains_a/b/c |
PAINS substructure filters |
brenk |
Unwanted functional groups |
nih |
NIH screening filters |
zinc |
ZINC structural filters |
glaxo, dundee, bms |
Pharma-derived alert sets |
mlsmr, inpharmatica, lint |
Additional screening sets |
nibr |
NIBR catalog (substructure) |
bredt |
Bredt rule violations (unstable structures) |
tox, toxicophore, carcinogen |
Toxicity patterns |
reactive_unstable_toxic |
Reactive/unstable groups |
unstable_graph |
Unstable molecular graphs |
cat = mc.catalogs.NamedCatalogs.brenk()
passes = mc.functional.catalog_filter(mols, catalogs=[cat], n_jobs=-1)
Compared to ZINC-15 percentile thresholds via ComplexityFilter or complexity_filter():
| Metric | Description |
|---|---|
bertz |
Bertz molecular complexity |
sas |
Synthetic accessibility score |
qed |
Quantitative Estimate of Drug-likeness |
clogp |
Calculated LogP |
whitlock |
Whitlock CT (rings, unsaturation, heteroatoms, chirality) |
barone |
Barone complexity |
smcm |
Synthetic complexity metric |
twc |
Total walk count |
mc.functional.complexity_filter(mols, complexity_metric="bertz", limit="99", n_jobs=-1)
limit="99" keeps compounds below the 99th percentile on ZINC-15.
Browse with mc.groups.list_default_chemical_groups():
| Group | Application |
|---|---|
privileged_scaffolds |
Common drug scaffolds |
common_warhead_covalent_inhibitors |
Covalent warhead patterns |
electrophilic_warheads_for_kinases |
Kinase covalent motifs |
rings_in_drugs |
Ring systems in approved drugs |
phase_2_hetereocyclic_rings |
Phase 2 heterocycles |
common_monomer_repeating_units |
Polymer/repeating units |
emerging_perfluoroalkyls |
PFAS-related patterns |
group = mc.groups.ChemicalGroup(groups=["privileged_scaffolds"])
group.has_match(mol)
Custom groups: provide a CSV via groups_db with columns smiles/smarts, name, group.
qf = mc.query.QueryFilter('MATCHRULE("rule_of_five") AND NOT HASALERT("pains")')
mask = qf(mols=mol_list, n_jobs=-1)
rules = mc.rules.RuleFilters(rule_list=["rule_of_oprea"])(mols, n_jobs=-1)
nibr = mc.structural.NIBRFilters()(mols, n_jobs=-1)
rules = mc.rules.RuleFilters(rule_list=["rule_of_druglike_soft"])(mols, n_jobs=-1)
alerts = mc.structural.CommonAlertsFilters()(mols, n_jobs=-1)
complexity = mc.functional.complexity_filter(mols, complexity_metric="bertz", limit="95", n_jobs=-1)
qf = mc.query.QueryFilter('MATCHRULE("rule_of_cns") AND HASPROP("tpsa", <=, 90)')
mask = qf(mols, n_jobs=-1)
rules = mc.rules.RuleFilters(rule_list=["rule_of_three"])(mols, n_jobs=-1)
complexity = mc.functional.complexity_filter(mols, complexity_metric="bertz", limit="90", n_jobs=-1)
Filters are guidelines, not absolutes: - ~10% of marketed oral drugs violate Ro5 - Natural products and prodrugs often fail standard rules - Passing filters does not guarantee clinical success
Combine with ML when appropriate:
rules_df = mc.rules.RuleFilters(rule_list=["rule_of_five"])(mols, n_jobs=-1)
filtered_mols = [m for m, ok in zip(mols, rules_df["pass_all"]) if ok]
# score filtered_mols with downstream ML model