Iptd_Dtmf

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.