MutationRx

MutationRx turns an unreliable computational method into a decision an oncology researcher can act on, by pairing physics with judgment. What we built. When a targeted cancer drug stops working, it is usually because the tumor mutated and reshaped the protein the drug was built to fit. MutationRx takes a tumor's driver mutation, docks a panel of 62 approved drugs against both the original and the mutated protein, and quantifies each binding shift with a 95% credible interval. It then adds the step software has never managed on its own: Claude reviews every hit for mechanistic plausibility and separates real leads from coincidental docking scores. What we found. Two results. Given only the structures, the tool independently reproduces the clinic: on a real, de-identified TCGA lung tumor it had never seen, the first-generation drugs lose their grip on the resistant mutant while osimertinib holds, the exact known picture. And statistics alone are not enough: across 300 approved drugs, the math flagged ten as confident, high-significance hits, and all ten were artifacts, including antidepressants and antibiotics with no business binding this target. Claude removed all ten and preserved one non-obvious, mechanistically sound lead, imatinib, that the numbers almost buried. Why it matters. When resistance appears, the question is practical and costly: what do we try next? Today that means months of bench work, and existing computational shortcuts are ignored because their hit lists cannot be trusted. MutationRx compresses that first decision from weeks to minutes and, more importantly, makes it trustworthy. The scarce skill was never generating a docking score; it is knowing which scores to believe, a contextual and mechanistic judgment that a frontier model provides and a script cannot. The payoff is fewer wasted experiments per dollar for researchers, precision-oncology reach that is not gated by a pharma budget for the field, and a shorter path from "my drug stopped working" to "here is the next thing to try" for patients.