Medchem Rules and Filters Catalog

Catalog of medicinal chemistry rules, alert sets, and filters in medchem 2.1.1 (reviewed 2026-10-01).

Thresholds below describe the implemented functions, which can differ from simplified literature summaries. See versioned basic-rule source and official API. Examples assume valid mol/mols supplied by the caller.

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)

Implementation reference: Walters & Namchuk (2003), Designing screens: how to make your hits a hit.

Implemented criteria: MW 200–500, cLogP −5 to 5, HBD 0–5, HBA 0–10, rotatable bonds 0–8, heavy atoms 15–50 (inclusive). The implementation computes formal charge but does not constrain it, despite the docstring advertising −2 to 2. Add an explicit charge bound when needed.

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

Implementation reference: Oprea (2000), Property distribution of drug-related chemical databases.

Implemented criteria: HBD 0–2, HBA 2–9, rotatable bonds 2–8, rings 1–4 (inclusive). This function does not constrain MW or cLogP; add explicit bounds if the project needs them.

mc.rules.basic_rules.rule_of_oprea(mol)

Rule of Leadlike (Soft)

Implemented criteria: MW 150–400, cLogP −3 to 4, HBD ≤4, HBA ≤7, TPSA ≤160 Ų, rotatable bonds ≤9, rigid bonds ≤30, rings ≤4, maximum ring-system size ≤18, carbons 3–35, heteroatoms 1–15, heteroatom/carbon ratio 0.1–1.1, formal charge −4 to 4, charged atoms ≤4, stereocenters ≤2.

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

Implemented criteria: MW ≤300, cLogP ≤3, HBD ≤3, HBA ≤3, rotatable bonds ≤3. There is no TPSA bound in rule_of_three. rule_of_three_extended adds TPSA ≤60 Ų and cLogP ≥−3 but permits HBA ≤6; it is not simply the base rule plus TPSA.

mc.rules.basic_rules.rule_of_three(mol)

Also available: rule_of_three_extended and rule_of_two. rule_of_four instead selects larger, lipophilic PPI-inhibitor space (MW ≥400, cLogP ≥4, rings ≥4, HBA ≥4); it is not a fragment filter.

CNS and Target-Class Rules

Rule of CNS

Implemented criteria: MW 135–582, cLogP −0.2 to 6.1, TPSA 3–118 Ų, HBD ≤3, HBA ≤5. These are inclusive heuristic bounds, not evidence of blood-brain barrier penetration.

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.catalog_filter(mols, catalogs=["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(alerts_set=["BMS", "Dundee", "Glaxo"])
df = alert_filter(mols=mol_list, n_jobs=-1)
# current common-alert status: exclude | 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)
# class rejects explicit exclusion alerts; also apply accumulated severity cutoff
passes = df["pass_filter"] & (df["severity"] < 10)

Or via functional API with max_severity=10.

Lilly Demerits (optional)

Install native upstream tools with medchem install-lilly; requires C++/make/zlib/Ruby and network access for installation. The native integration uses upstream 2.1 rules and was not executed locally. The wrapper requires native pass_filter=True and either a missing score or a score strictly below max_demerits=160 (a score of 160 fails). Native hard-rejection and atom-count rules also apply.

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" uses a strict upper threshold in the molecule's molecular-weight bin. Available ZINC-15 labels are median, 90, 99, 999 (99.9%), and max; 95 is invalid. QED is also compared with an upper cutoff, so this is not a high-QED selector. Scores that are NaN pass upstream; check finiteness if applicable. The new mc.complexity.SPS(mol) computes normalized SpacialScore; filtering with spacialscore requires custom threshold statistics.

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 both smiles and smarts, plus name and group. SMILES/SMARTS and exact-match settings can yield different answers. ChemicalGroup.filter(names) narrows pattern names in place; get_matches(mol) returns a DataFrame. Query HASGROUP requires an individual functional-group name, not one of these collection names.

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(alerts_set=["BMS", "Dundee", "Glaxo"])(mols, n_jobs=-1)
complexity = mc.functional.complexity_filter(mols, complexity_metric="bertz", limit="90", 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:

• Some marketed oral drugs violate Ro5; the fraction depends on the dataset and rule definition
• Natural products and prodrugs often fail standard rules
• Passing filters does not establish clinical success, toxicity, permeability, or synthesis feasibility
• PAINS alerts require assay-specific counter-screens; a motif match alone does not prove interference
• Record original structures and any salt, protonation, tautomer, or stereochemistry normalization

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. J Comput Aided Mol Des (2000) 14:251–264; https://doi.org/10.1023/a:1008130001697
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 & Namchuk M. (2003), Designing screens: how to make your hits a hit; https://pubmed.ncbi.nlm.nih.gov/12669025/
7. Schuffenhauer A et al. J Med Chem (2020) — NIBR screening deck
8. Doak BC et al. (2015) — Beyond Rule of Five