Medchem Rules and Filters Catalog

Catalog of medicinal chemistry rules, alert sets, and filters in medchem 2.0.5.

Table of Contents

1. Drug-Likeness Rules
2. Lead-Likeness Rules
3. Fragment Rules
4. CNS and Target-Class Rules
5. Structural Alert Filters
6. Named Catalogs
7. Complexity Metrics
8. Chemical Group Collections
9. Filter Selection Guidelines

Drug-Likeness Rules

Rule of Five (Lipinski)

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"])

Rule of Five Beyond

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)

Rule of Veber

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)

REOS (Rapid Elimination Of Swill)

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)

Egan, Ghose, Pfizer, GSK, Xu

Additional literature filters available as rule_of_egan, rule_of_ghose, rule_of_pfizer_3_75, rule_of_gsk_4_400, rule_of_xu.

Rule of Druglike (Soft)

Combined soft drug-likeness criteria:

mc.rules.basic_rules.rule_of_druglike_soft(mol)

Lead-Likeness Rules

Rule of Oprea

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)

Rule of Leadlike (Soft)

Criteria: MW 250–450, LogP −3 to 4, rotatable bonds ≤ 10

mc.rules.basic_rules.rule_of_leadlike_soft(mol)

Fragment Rules

Rule of Three

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.

CNS and Target-Class Rules

Rule of CNS

Criteria: MW ≤ 450, LogP −1 to 5, HBD ≤ 2, TPSA ≤ 90 Ų

mc.rules.basic_rules.rule_of_cns(mol)

Rule of Respiratory

Target-class filter for respiratory drugs:

mc.rules.basic_rules.rule_of_respiratory(mol)

Generative Design Rules

For ML-generated molecules:

mc.rules.basic_rules.rule_of_generative_design(mol)
mc.rules.basic_rules.rule_of_generative_design_strict(mol)

Structural Alert Filters

PAINS (Pan Assay INterference compoundS)

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.

Common Alerts Filters

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

NIBR Filters

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.

Lilly Demerits (optional)

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)

Named Catalogs

Available via mc.catalogs.list_named_catalogs() and NamedCatalogs static methods:

cat = mc.catalogs.NamedCatalogs.brenk()
passes = mc.functional.catalog_filter(mols, catalogs=[cat], n_jobs=-1)

Complexity Metrics

Compared to ZINC-15 percentile thresholds via ComplexityFilter or complexity_filter():

mc.functional.complexity_filter(mols, complexity_metric="bertz", limit="99", n_jobs=-1)

limit="99" keeps compounds below the 99th percentile on ZINC-15.

Chemical Group Collections

Browse with mc.groups.list_default_chemical_groups():

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.

Filter Selection Guidelines

Initial Screening (HTS deck)

qf = mc.query.QueryFilter('MATCHRULE("rule_of_five") AND NOT HASALERT("pains")')
mask = qf(mols=mol_list, n_jobs=-1)

Hit-to-Lead

rules = mc.rules.RuleFilters(rule_list=["rule_of_oprea"])(mols, n_jobs=-1)
nibr = mc.structural.NIBRFilters()(mols, n_jobs=-1)

Lead Optimization

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)

CNS Targets

qf = mc.query.QueryFilter('MATCHRULE("rule_of_cns") AND HASPROP("tpsa", <=, 90)')
mask = qf(mols, n_jobs=-1)

Fragment-Based Discovery

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)

Important Considerations

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

References

1. Lipinski CA et al. Adv Drug Deliv Rev (1997) 23:3–25
2. Veber DF et al. J Med Chem (2002) 45:2615–2623
3. Oprea TI et al. J Chem Inf Comput Sci (2001) 41:1308–1315
4. Congreve M et al. Drug Discov Today (2003) 8:876–877
5. Baell JB & Holloway GA. J Med Chem (2010) 53:2719–2740
6. Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255–271
7. Schuffenhauer A et al. J Med Chem (2020) — NIBR screening deck
8. Doak BC et al. (2015) — Beyond Rule of Five