Apply drug-likeness rules and structural alerts to chemical compounds.
npx skills add https://github.com/felixboehm/biochem-allergy --skill medchem-felixboehm
Please help me install this Agent Skill. Skill: medchem Source: https://github.com/felixboehm/biochem-allergy/tree/main/.claude/skills/medchem Command: npx skills add https://github.com/felixboehm/biochem-allergy --skill medchem-felixboehm
This Skill streamlines drug discovery by providing robust tools to filter and prioritize chemical compounds based on medicinal chemistry rules and structural properties, saving researchers time and resources.
Use the medchem skill to apply the Rule of Five and common alerts filter to a list of SMILES strings.
High-intent search queries and answers about installing and using this skill.
To filter a compound library for drug-likeness, you can apply established rules like Lipinski, Veber, and Oprea using Python libraries such as RDKit and datamol. This systematically evaluates small molecules to assess compound quality.
Structural alerts in medicinal chemistry are problematic substructures that can cause assay interference or toxicity. You detect them by applying PAINS, NIBR, and Lilly Demerits filters to chemical compounds during lead optimization.
Yes, you can use RDKit and datamol to triage a large compound library for drug discovery. The process applies medicinal chemistry filters and drug-likeness rules to identify a smaller, high-quality set for experimental validation.
The best way to prioritize chemical compounds for lead optimization is to assess molecular properties by applying drug-likeness rules and structural alert filters. This removes compounds with known liabilities and prioritizes high-quality candidates.
Applying medicinal chemistry filters requires a list of SMILES strings as input data. You use these chemical compound representations to apply the Rule of Five and common alerts filters for systematic evaluation.
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