What problem does it solve? Deciding whether a biological target is worth pursuing in drug discovery requires synthesizing evidence from dozens of databases (OpenTargets, ChEMBL, PDB, GTEx, DepMap, FDA, and more). This Skill automates that multi-source evidence gathering and produces a quantitative Target Validation Score (0-100) with a GO/NO-GO recommendation before committing to wet-lab work. ## Core Features & Use Cases - 10-Phase Validation Pipeline: Covers target disambiguation, disease association, druggability, chemical matter, clinical precedent, safety, pathway context, validation evidence, structural insights, and a validation roadmap. - Quantitative Scoring System: Scores five dimensions (disease association 30, druggability 25, safety 20, clinical precedent 15, validation evidence 10) with T1-T4 evidence grading and four priority tiers. - Structured Report Output: Generates a complete markdown validation report with executive summary, scorecard, completeness checklist, risk assessment, and recommended experiments. - Use Case: Ask "Is KRAS a druggable target for pancreatic cancer?" and receive a full evidence-based report with a validation score, tier classification, and GO/NO-GO recommendation. ## Quick Start Ask the assistant to validate whether a specific gene or protein is a good drug target for a given disease, optionally specifying a therapeutic modality such as small molecule or antibody.
