See Da Protein

Biologists call a protein interaction map a hairball: hundreds of contacts, each one months of bench work, and no way to know which one matters. The field largely abandoned these maps. Cartograph reads them. Ask it what a viral protein is hitting that hasn't been mapped, and a deterministic graph layer walks a length-3 path and proposes the missing edge. Claude then reads the literature behind it and returns a mechanism where every clause opens a real paper, the interface residues are computed from crystal coordinates, and the output is the exact mutation to test at the bench. We hid 57 real interactions before the system ever ran, sealed in the first commit. It recovers them at precision@20 = 0.45, ROC-AUC 0.845. Cross-coronavirus conservation, added as a prior, doubled precision@10 to 0.60. A structural channel added nothing — and that result is in the repo, because a system that only reports good news isn't one you can publish. On new data, an Evidence Agent runs live — and a deterministic gate, not a prompt, deletes any sentence whose citation Claude wasn't actually given. Every AI tool in biology asks you to trust it. This one asks you to check it.

Team

Placement

Participant