# Target Sniper

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Qiang Zhang](https://cerebralvalley.ai/u/qz26)
- **GitHub:** https://github.com/qzhrr/AI-Driven-Therapeutic-Target-Discovery
- **Demo video:** https://youtu.be/gbQafLF4UmQ
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/254

I built a cross-dataset integration pipeline to nominate novel, small-molecule-druggable glioblastoma (GBM) targets. Starting from DepMap CRISPR dependencies (72 glioma vs. 1,106 non-glioma lines; 17,916 genes), we isolated 283 glioma-selective dependencies, genes essential in glioma but not pan-essential. Each candidate was scored across seven evidence axes: genetic dependency, selectivity, synthetic lethality, tumor-vs-microenvironment expression (GBmap single-cell), therapeutic window (GTEx and normal-brain safety), tumor enrichment (TCGA-GBM), and small-molecule tractability (ChEMBL, Open Targets, DGIdb, UniProt). A single model (Claude) assigned category weights (100 runs, blind to candidate scores) and nominated targets (100 runs); a gene-name-masking control separated evidence-driven picks from name recognition. The pipeline recovered known GBM biology, 4 of 5 pre-declared positive controls in the top 12 (p=3.1e-5), and rejected the canonical false-positive EGFR. Three leads survived scrutiny: VRK1, ELAVL1/HuR (degrader-tractable), and KIF2C, a novel 9p21-deletion-selective dependency (candidate synthetic lethality, computationally derived). These are computational nominations awaiting experimental validation, from a 72-line, 2D-culture cohort scored by a single model. All work was performed by Claude for Science.

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