# BibimbapKiller

- **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:** [Justin Kim](https://cerebralvalley.ai/u/MasalaKimchi)
- **GitHub:** https://github.com/MasalaKimchi/cell-state-reachability
- **Demo video:** https://www.youtube.com/watch?v=GJbxLxYUBMo
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/60

What if a model could say the one word every drug-discovery pipeline needs but no other tool can utter — "unreachable"? 

Cell-state-reachability reframes perturbation biology around a new question: not "what will this gene knockout do?" but "can we actually steer a cell from state A to state B — and should you believe the answer?" 

We surveyed 91 perturbation-prediction methods; none return a feasibility verdict. Ours does. Built on public Perturb-seq screens (Replogle K562/RPE1, Norman combinatorial doubles), it produces a map of reachable cell-state space plus the falsification machinery that flags which regions you're allowed to trust: additivity bounds for multi-gene edits, weak-instrument confidence intervals, and negative-control and construct-validity checks.

The payoff is targets a supervised ranker structurally can't nominate — like ZAP70: small-molecule-tractable, genetically associated, yet undrugged.

The stance is AI-as-instrument, not oracle. The deliverable isn't a gene list; it's understanding of the data's structure earned inside a falsification loop — fitting for a field whose own 2025 benchmark shows deep models don't beat linear baselines.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/60. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt