gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/medchem/references/rules_catalog.md

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

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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:

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

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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:

mc.rules.basic_rules.rule_of_veber(mol)

Notes:

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

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Rule of Drug

Purpose: Combined drug-likeness assessment

Criteria:

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

Usage:

mc.rules.basic_rules.rule_of_drug(mol)

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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:

mc.rules.basic_rules.rule_of_reos(mol)

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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:

mc.rules.basic_rules.golden_triangle(mol)

Notes:

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

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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:

mc.rules.basic_rules.rule_of_oprea(mol)

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

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Rule of Leadlike (Soft)

Purpose: Permissive lead-like criteria

Criteria:

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

Usage:

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:

mc.rules.basic_rules.rule_of_leadlike_strict(mol)

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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:

mc.rules.basic_rules.rule_of_three(mol)

Notes:

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

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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:

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

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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:

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

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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:

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

Return Format:

{
    "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:

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

Return Format: Boolean list (True = passes)

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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:

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

Return Format:

{
    "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:

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:

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:

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:

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

---

Custom SMARTS Patterns

Define custom structural patterns using SMARTS:

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)

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

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

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

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

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:

# 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