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Medchem Rules and Filters Catalog

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Medchem Rules and Filters Catalog

Comprehensive catalog of all available medicinal chemistry rules, structural alerts, and filters in medchem.

Table of Contents

  1. Drug-Likeness Rules
  2. Lead-Likeness Rules
  3. Fragment Rules
  4. CNS Rules
  5. Structural Alert Filters
  6. Chemical Group Patterns

Drug-Likeness Rules

Rule of Five (Lipinski)

Reference: Lipinski et al., Adv Drug Deliv Rev (1997) 23:3-25

Purpose: Predict oral bioavailability

Criteria:

  • Molecular Weight ≤ 500 Da
  • LogP ≤ 5
  • Hydrogen Bond Donors ≤ 5
  • Hydrogen Bond Acceptors ≤ 10

Usage:

python
mc.rules.basic_rules.rule_of_five(mol)

Notes:

  • One of the most widely used filters in drug discovery
  • About 90% of orally active drugs comply with these rules
  • Exceptions exist, especially for natural products and antibiotics

Rule of Veber

Reference: Veber et al., J Med Chem (2002) 45:2615-2623

Purpose: Additional criteria for oral bioavailability

Criteria:

  • Rotatable Bonds ≤ 10
  • Topological Polar Surface Area (TPSA) ≤ 140 Ų

Usage:

python
mc.rules.basic_rules.rule_of_veber(mol)

Notes:

  • Complements Rule of Five
  • TPSA correlates with cell permeability
  • Rotatable bonds affect molecular flexibility

Rule of Drug

Purpose: Combined drug-likeness assessment

Criteria:

  • Passes Rule of Five
  • Passes Veber rules
  • Does not contain PAINS substructures

Usage:

python
mc.rules.basic_rules.rule_of_drug(mol)

REOS (Rapid Elimination Of Swill)

Reference: Walters & Murcko, Adv Drug Deliv Rev (2002) 54:255-271

Purpose: Filter out compounds unlikely to be drugs

Criteria:

  • Molecular Weight: 200-500 Da
  • LogP: -5 to 5
  • Hydrogen Bond Donors: 0-5
  • Hydrogen Bond Acceptors: 0-10

Usage:

python
mc.rules.basic_rules.rule_of_reos(mol)

Golden Triangle

Reference: Johnson et al., J Med Chem (2009) 52:5487-5500

Purpose: Balance lipophilicity and molecular weight

Criteria:

  • 200 ≤ MW ≤ 50 × LogP + 400
  • LogP: -2 to 5

Usage:

python
mc.rules.basic_rules.golden_triangle(mol)

Notes:

  • Defines optimal physicochemical space
  • Visual representation resembles a triangle on MW vs LogP plot

Lead-Likeness Rules

Rule of Oprea

Reference: Oprea et al., J Chem Inf Comput Sci (2001) 41:1308-1315

Purpose: Identify lead-like compounds for optimization

Criteria:

  • Molecular Weight: 200-350 Da
  • LogP: -2 to 4
  • Rotatable Bonds ≤ 7
  • Number of Rings ≤ 4

Usage:

python
mc.rules.basic_rules.rule_of_oprea(mol)

Rationale: Lead compounds should have "room to grow" during optimization

Rule of Leadlike (Soft)

Purpose: Permissive lead-like criteria

Criteria:

  • Molecular Weight: 250-450 Da
  • LogP: -3 to 4
  • Rotatable Bonds ≤ 10

Usage:

python
mc.rules.basic_rules.rule_of_leadlike_soft(mol)

Rule of Leadlike (Strict)

Purpose: Restrictive lead-like criteria

Criteria:

  • Molecular Weight: 200-350 Da
  • LogP: -2 to 3.5
  • Rotatable Bonds ≤ 7
  • Number of Rings: 1-3

Usage:

python
mc.rules.basic_rules.rule_of_leadlike_strict(mol)

Fragment Rules

Rule of Three

Reference: Congreve et al., Drug Discov Today (2003) 8:876-877

Purpose: Screen fragment libraries for fragment-based drug discovery

Criteria:

  • Molecular Weight ≤ 300 Da
  • LogP ≤ 3
  • Hydrogen Bond Donors ≤ 3
  • Hydrogen Bond Acceptors ≤ 3
  • Rotatable Bonds ≤ 3
  • Polar Surface Area ≤ 60 Ų

Usage:

python
mc.rules.basic_rules.rule_of_three(mol)

Notes:

  • Fragments are grown into leads during optimization
  • Lower complexity allows more starting points

CNS Rules

Rule of CNS

Purpose: Central nervous system drug-likeness

Criteria:

  • Molecular Weight ≤ 450 Da
  • LogP: -1 to 5
  • Hydrogen Bond Donors ≤ 2
  • TPSA ≤ 90 Ų

Usage:

python
mc.rules.basic_rules.rule_of_cns(mol)

Rationale:

  • Blood-brain barrier penetration requires specific properties
  • Lower TPSA and HBD count improve BBB permeability
  • Tight constraints reflect CNS challenges

Structural Alert Filters

PAINS (Pan Assay INterference compoundS)

Reference: Baell & Holloway, J Med Chem (2010) 53:2719-2740

Purpose: Identify compounds that interfere with assays

Categories:

  • Catechols
  • Quinones
  • Rhodanines
  • Hydroxyphenylhydrazones
  • Alkyl/aryl aldehydes
  • Michael acceptors (specific patterns)

Usage:

python
mc.rules.basic_rules.pains_filter(mol)
# Returns True if NO PAINS found

Notes:

  • PAINS compounds show activity in multiple assays through non-specific mechanisms
  • Common false positives in screening campaigns
  • Should be deprioritized in lead selection

Common Alerts Filters

Source: Derived from ChEMBL curation and medicinal chemistry literature

Purpose: Flag common problematic structural patterns

Alert Categories:

  1. Reactive Groups

    • Epoxides
    • Aziridines
    • Acid halides
    • Isocyanates

  2. Metabolic Liabilities

    • Hydrazines
    • Thioureas
    • Anilines (certain patterns)

  3. Aggregators

    • Polyaromatic systems
    • Long aliphatic chains

  4. Toxicophores

    • Nitro aromatics
    • Aromatic N-oxides
    • Certain heterocycles

Usage:

python
alert_filter = mc.structural.CommonAlertsFilters()
has_alerts, details = alert_filter.check_mol(mol)

Return Format:

python
{
    "has_alerts": True,
    "alert_details": ["reactive_epoxide", "metabolic_hydrazine"],
    "num_alerts": 2
}

NIBR Filters

Source: Novartis Institutes for BioMedical Research

Purpose: Industrial medicinal chemistry filtering rules

Features:

  • Proprietary filter set developed from Novartis experience
  • Balances drug-likeness with practical medicinal chemistry
  • Includes both structural alerts and property filters

Usage:

python
nibr_filter = mc.structural.NIBRFilters()
results = nibr_filter(mols=mol_list, n_jobs=-1)

Return Format: Boolean list (True = passes)

Lilly Demerits Filter

Reference: Based on Eli Lilly medicinal chemistry rules

Source: 275 structural patterns accumulated over 18 years

Purpose: Identify assay interference and problematic functionalities

Mechanism:

  • Each matched pattern adds demerits
  • Molecules with >100 demerits are rejected
  • Some patterns add 10-50 demerits, others add 100+ (instant rejection)

Demerit Categories:

  1. High Demerits (>50):

    • Known toxic groups
    • Highly reactive functionalities
    • Strong metal chelators

  2. Medium Demerits (20-50):

    • Metabolic liabilities
    • Aggregation-prone structures
    • Frequent hitters

  3. Low Demerits (5-20):

    • Minor concerns
    • Context-dependent issues

Usage:

python
lilly_filter = mc.structural.LillyDemeritsFilters()
results = lilly_filter(mols=mol_list, n_jobs=-1)

Return Format:

python
{
    "demerits": 35,
    "passes": True,  # (demerits ≤ 100)
    "matched_patterns": [
        {"pattern": "phenolic_ester", "demerits": 20},
        {"pattern": "aniline_derivative", "demerits": 15}
    ]
}

Chemical Group Patterns

Hinge Binders

Purpose: Identify kinase hinge-binding motifs

Common Patterns:

  • Aminopyridines
  • Aminopyrimidines
  • Indazoles
  • Benzimidazoles

Usage:

python
group = mc.groups.ChemicalGroup(groups=["hinge_binders"])
has_hinge = group.has_match(mol_list)

Application: Kinase inhibitor design

Phosphate Binders

Purpose: Identify phosphate-binding groups

Common Patterns:

  • Basic amines in specific geometries
  • Guanidinium groups
  • Arginine mimetics

Usage:

python
group = mc.groups.ChemicalGroup(groups=["phosphate_binders"])

Application: Kinase inhibitors, phosphatase inhibitors

Michael Acceptors

Purpose: Identify electrophilic Michael acceptor groups

Common Patterns:

  • α,β-Unsaturated carbonyls
  • α,β-Unsaturated nitriles
  • Vinyl sulfones
  • Acrylamides

Usage:

python
group = mc.groups.ChemicalGroup(groups=["michael_acceptors"])

Notes:

  • Can be desirable for covalent inhibitors
  • Often flagged as reactive alerts in screening

Reactive Groups

Purpose: Identify generally reactive functionalities

Common Patterns:

  • Epoxides
  • Aziridines
  • Acyl halides
  • Isocyanates
  • Sulfonyl chlorides

Usage:

python
group = mc.groups.ChemicalGroup(groups=["reactive_groups"])

Custom SMARTS Patterns

Define custom structural patterns using SMARTS:

python
custom_patterns = {
    "my_warhead": "[C;H0](=O)C(F)(F)F",  # Trifluoromethyl ketone
    "my_scaffold": "c1ccc2c(c1)ncc(n2)N",  # Aminobenzimidazole
}

group = mc.groups.ChemicalGroup(
    groups=["hinge_binders"],
    custom_smarts=custom_patterns
)

Filter Selection Guidelines

Initial Screening (High-Throughput)

Recommended filters:

  • Rule of Five
  • PAINS filter
  • Common Alerts (permissive settings)
python
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_five", "pains_filter"])
alert_filter = mc.structural.CommonAlertsFilters()

Hit-to-Lead

Recommended filters:

  • Rule of Oprea or Leadlike (soft)
  • NIBR filters
  • Lilly Demerits
python
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_oprea"])
nibr_filter = mc.structural.NIBRFilters()
lilly_filter = mc.structural.LillyDemeritsFilters()

Lead Optimization

Recommended filters:

  • Rule of Drug
  • Leadlike (strict)
  • Full structural alert analysis
  • Complexity filters
python
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_drug", "rule_of_leadlike_strict"])
alert_filter = mc.structural.CommonAlertsFilters()
complexity_filter = mc.complexity.ComplexityFilter(max_complexity=400)

CNS Targets

Recommended filters:

  • Rule of CNS
  • Reduced PAINS criteria (CNS-focused)
  • BBB permeability constraints
python
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_cns"])
constraints = mc.constraints.Constraints(
    tpsa_max=90,
    hbd_max=2,
    mw_range=(300, 450)
)

Fragment-Based Drug Discovery

Recommended filters:

  • Rule of Three
  • Minimal complexity
  • Basic reactive group check
python
rfilter = mc.rules.RuleFilters(rule_list=["rule_of_three"])
complexity_filter = mc.complexity.ComplexityFilter(max_complexity=250)

Important Considerations

False Positives and False Negatives

Filters are guidelines, not absolutes:

  1. False Positives (good drugs flagged):

    • ~10% of marketed drugs fail Rule of Five
    • Natural products often violate standard rules
    • Prodrugs intentionally break rules
    • Antibiotics and antivirals frequently non-compliant

  2. False Negatives (bad compounds passing):

    • Passing filters doesn't guarantee success
    • Target-specific issues not captured
    • In vivo properties not fully predicted

Context-Specific Application

Different contexts require different criteria:

  • Target Class: Kinases vs GPCRs vs ion channels have different optimal spaces
  • Modality: Small molecules vs PROTACs vs molecular glues
  • Administration Route: Oral vs IV vs topical
  • Disease Area: CNS vs oncology vs infectious disease
  • Stage: Screening vs hit-to-lead vs lead optimization

Complementing with Machine Learning

Modern approaches combine rules with ML:

python
# Rule-based pre-filtering
rule_results = mc.rules.RuleFilters(rule_list=["rule_of_five"])(mols)
filtered_mols = [mol for mol, r in zip(mols, rule_results) if r["passes"]]

# ML model scoring on filtered set
ml_scores = ml_model.predict(filtered_mols)

# Combined decision
final_candidates = [
    mol for mol, score in zip(filtered_mols, ml_scores)
    if score > threshold
]

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. Johnson TW et al. J Med Chem (2009) 52:5487-5500
  7. Walters WP & Murcko MA. Adv Drug Deliv Rev (2002) 54:255-271
  8. Hann MM & Oprea TI. Curr Opin Chem Biol (2004) 8:255-263
  9. Rishton GM. Drug Discov Today (1997) 2:382-384