Medchem

Apply established drug-likeness rules to molecules using the medchem.rules module.

Available Rules:

• Rule of Five (Lipinski)
• Rule of Oprea
• Rule of CNS
• Rule of leadlike (soft and strict)
• Rule of three
• Rule of Reos
• Rule of drug
• Rule of Veber
• Golden triangle
• PAINS filters

Single Rule Application:

Multiple Rules with RuleFilters:

Result Format: Results are returned as dictionaries with pass/fail status and detailed information for each rule.

2. Structural Alert Filters

Detect potentially problematic structural patterns using the medchem.structural module.

Available Filters:

1. Common Alerts - General structural alerts derived from ChEMBL curation and literature
2. NIBR Filters - Novartis Institutes for BioMedical Research filter set
3. Lilly Demerits - Eli Lilly's demerit-based system (275 rules, molecules rejected at >100 demerits)

Common Alerts:

NIBR Filters:

Lilly Demerits:

3. Functional API for High-Level Operations

The medchem.functional module provides convenient functions for common workflows.

Quick Filtering:

4. Chemical Groups Detection

Identify specific chemical groups and functional groups using medchem.groups.

Available Groups:

• Hinge binders
• Phosphate binders
• Michael acceptors
• Reactive groups
• Custom SMARTS patterns

5. Named Catalogs

Access curated collections of chemical structures through medchem.catalogs.

Available Catalogs:

• Functional groups
• Protecting groups
• Common reagents
• Standard fragments

6. Molecular Complexity

Calculate complexity metrics that approximate synthetic accessibility using medchem.complexity.

Common Metrics:

• Bertz complexity
• Whitlock complexity
• Barone complexity

7. Constraints Filtering

Apply custom property-based constraints using medchem.constraints.

Example Constraints:

• Molecular weight ranges
• LogP bounds
• TPSA limits
• Rotatable bond counts

8. Medchem Query Language

Use a specialized query language for complex filtering criteria.

Query Examples:

# Molecules passing Ro5 AND not having common alerts
"rule_of_five AND NOT common_alerts"

# CNS-like molecules with low complexity
"rule_of_cns AND complexity < 400"

# Leadlike molecules without Lilly demerits
"rule_of_leadlike AND lilly_demerits == 0"

Workflow Patterns

Pattern 1: Initial Triage of Compound Library

Filter a large compound collection to identify drug-like candidates.

Pattern 2: Lead Optimization Filtering

Apply stricter criteria during lead optimization.

Pattern 3: Identify Specific Chemical Groups

Find molecules containing specific functional groups or scaffolds.

Best Practices

1. Context Matters: Don't blindly apply filters. Understand the biological target and chemical space.

2. Combine Multiple Filters: Use rules, structural alerts, and domain knowledge together for better decisions.

3. Use Parallelization: For large datasets (>1000 molecules), always use n_jobs=-1 for parallel processing.

4. Iterative Refinement: Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed.

5. Document Filtering Decisions: Track which molecules were filtered out and why for reproducibility.

6. Validate Results: Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules.

7. Consider Prodrugs: Molecules designed as prodrugs may intentionally violate standard medicinal chemistry rules.

Output

• Filtering Reports: Detailed pass/fail status for established drug-likeness rules (e.g., Lipinski's Rule of Five, Veber's rules).
• Structural Alert Logs: Identification of problematic substructures, including PAINS, NIBR filters, and Lilly demerit scores.
• Molecular Metrics: Calculated complexity indices (Bertz, Whitlock, etc.) and physicochemical properties (LogP, MW, TPSA).
• Prioritized Compound Lists: A curated set of molecules that have passed the specified medicinal chemistry triage for lead optimization.