RESISTOR

Most AI drug discovery tools answer "which compound kills this bacteria today" — a crowded, reactive question. RESISTOR asks a different one: which mutations will this bacteria evolve next to survive our best drugs, before it happens in the wild. We built a genetic-algorithm and particle-swarm search loop — adapted from our prior project AETHER — to drive a trained graph neural network that scores binding-affinity and stability deltas for thousands of point mutations on a real, published protein-drug pair (S. aureus DHFR, PDB 2w9g). An attribution layer explains why each high-risk mutation matters structurally, and a live Fireworks AI integration turns that into a plain-language forecast report. Everything is tied together by an orchestration and verification pipeline that confirms every result is fresh and real before it's shown. We ran into real obstacles — AMD cloud access failures, a counterintuitive CPU-beating-GPU benchmark, twice-exposed API keys — and we disclosed all of it rather than hiding it, including a proven parity test and device-agnostic, ROCm-ready code. Our model independently rediscovers F98Y, a real clinical resistance mutation, computed fresh every run — proof this isn't scripted.