# Iptd_Dtmf

- **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:** [Weicheng Li](https://cerebralvalley.ai/u/Weicheng)
- **GitHub:** https://github.com/WeichengLite/Induced-Proximity-Target-Discover-Agent
- **Demo video:** https://youtu.be/Af4ac4-Ct9Q
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/223

Most oncogenic drivers — transcription factors, chromatin modifiers, scaffolds, DNA-repair proteins — are "occupancy-undruggable": they have no pocket a classical inhibitor can bind. Induced proximity (molecular glues, bifunctional degraders) sidesteps this by recruiting the protein to an E3 ligase for degradation, but finding which driver→E3 pair is worth pursuing is a needle-in-a-haystack problem across a whole interactome.

I built an autonomous agent that reasons over a cancer protein–protein interaction map to surface, for each undruggable driver, the ligandable E3 ligase(s) that could plausibly be recruited to degrade it — as a ranked, fully auditable list of hypotheses. It is a budget-aware scheduler, not a brute-force pipeline: a cheap network prior (path evidence × E3-tier × co-expression × compartment match) runs first, then a rationed structure-prediction step (Boltz-2 fast pass → AF3 sharpening) is spent top-down, then a lysine-geometry gate (catalytic-Cys → substrate-Lys for RBR ligases), then a literature falsifier pass that labels known degraders as positive controls and discards known negatives — each decision logged with provenance. Every score is a transparent multiplicative breakdown, never a bare scalar, and no PPI edge is ever fabricated (enforced by tests).

During the hackathon I moved it from a synthetic demo onto real data. Using Claude Science I fetched the CCMI breast-cancer AP-MS interactome (Kim et al., Science 2021; NDEx + ProteomeXchange PXD019639/PXD025931), reshaped it into the agent's schema (522 proteins, 1,654 scored interactions), and annotated every node with UniProt subcellular localization, Open Targets small-molecule tractability, and E3-ligase tier — then layered in 2,449 experimentally-observed ubiquitylation sites and 14,904 phospho-sites from the matched PTM dataset. The Ub sites are real accessible lysines, so they can replace the agent's simulated geometry gate with measured evidence.

What I found: 12 of the 39 drivers are undruggable or hard-to-drug, and they gain 478 tumor-specific interactions relative to normal breast cells. Filtering to undruggable/hard drivers with high-confidence tumor-specific partners yields 39 priority induced-proximity candidates. The standout is PALB2–KEAP1: a hard-to-drug DNA-repair driver acquiring a tumor-context interaction with a ligandable E3 substrate receptor — a genuine, testable degradation handle. The agent runs end-to-end on this real data with a per-hypothesis wet-lab test attached.

Why it matters: it turns a raw interaction landscape into a short, checkable list of degrader hypotheses a bench scientist can act on — with auditability (every claim traces to a tool call) prioritized over completeness.

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Markdown version of https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/223. Site index for agents: https://cerebralvalley.ai/llms.txt · full text: https://cerebralvalley.ai/llms-full.txt