domains / families / science / medchem

medchem

combined

Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering. Applies medicinal chemistry filters for compound triage, using drug-likeness rules (Lipinski, Veber, CNS), structural alert catalogs (PAINS, NIBR, ChEMBL), complexity metrics, and the medchem query language for library filtering.

When to use

• Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries
• Filtering molecules by structural alerts or PAINS patterns
• Prioritizing compounds for lead optimization
• Assessing compound quality and medicinal chemistry properties
• Detecting reactive or problematic functional groups
• Calculating molecular complexity metrics
• Applying drug-likeness rules (Lipinski, Veber, CNS, lead-like) to compound libraries
• Filtering molecules by structural alerts, PAINS, or NIBR screening-deck rules

Skill page

Conflict

The K-Dense copy is the most technically precise and verified (pinned versions, DataFrame return types, Lilly native build requirements, and specific API distinctions like catalog_filter vs alert_filter). The other three copies (Vibe-Skills, aipoch, OpenClaw) are near-identical to each other and provide a broader, higher-level overview including Lilly Demerits, Chemical Groups, Complexity, and Constraints modules that the K-Dense copy truncates or omits in detail. They disagree on result formats (DataFrames vs dictionaries) and installation specifics (pinned vs latest). Combining is necessary to capture the verified low-level API details from K-Dense and the comprehensive module coverage from the others.