Apply Lipinski, Veber, CNS, leadlike, PAINS filters to prioritize molecular libraries.
npx skills add https://github.com/jackspace/ClaudeSkillz --skill medchem
Please help me install this Agent Skill. Skill: medchem Source: https://github.com/jackspace/ClaudeSkillz/tree/main/skills/scientific-pkg-medchem Command: npx skills add https://github.com/jackspace/ClaudeSkillz --skill medchem
This Skill helps chemists apply drug-likeness rules, PAINS filters, and structure-based alerts to prune libraries and prioritize compounds for screening.
Run a prebuilt filtering script to screen a compound library and export results.
High-intent search queries and answers about installing and using this skill.
Drug-likeness filtering applies rule-based checks like Lipinski's Rule of Five, Veber, CNS, and leadlike criteria to identify compounds meeting pharmacological standards. This Skill automates batch screening of molecular libraries to prioritize candidates with favorable absorption, distribution, and synthetic feasibility for lead optimization.
PAINS (Pan-Assay Interference Compounds) filters and structural alerts detect problematic molecular patterns that cause false positives in screening or lead to poor drug properties. This Skill identifies these patterns automatically to remove liability compounds before costly experimental validation.
Yes. This Skill calculates molecular complexity metrics and constraint-based properties, enabling you to rank and filter candidates by synthetic accessibility, structural diversity, and complexity thresholds alongside drug-likeness rules for efficient lead optimization workflows.
This Skill executes high-throughput batch filtering across rule sets including Ro5, Veber, CNS, leadlike, PAINS, and structural alerts in a single pass, producing per-molecule rule results and filtered candidate sets for downstream analysis and export.
Drug-likeness rules (Lipinski, Veber, CNS) predict oral bioavailability through property thresholds; structural alerts and PAINS detect specific reactive or problematic substructures that compromise safety or assay reliability. Both are complementary filters applied here to maximize candidate quality.
pradyumnasagarRead and write molecular file formats with RDKit and Open Babel.
001TMFIngest wet-lab readouts and calibrate in-silico predictions against lab outcomes.
itallstartedwithaideaConvert SMILES/SDF molecular structures into computed descriptors and screening outputs.
SologaApplies medicinal chemistry filters to compound libraries for drug prioritization.
