Stress-Testing AI Co-Folding Screens: Lessons from a Keap1–Nrf2 Case Study
- Multi-stage virtual screening cascades that combine AI co-folding models with learned-affinity (ML) scoring are now standard practice in computational drug discovery.
- Each stage's output score is typically taken at face value and used to advance or discard candidates without independent validation of what it actually captures.
- This preprint systematically evaluates, stage by stage, whether cascade scores provide the information practitioners need to trust them, using the Keap1-Nrf2 protein-protein interaction as a case-study target.
- The authors characterize failure modes that arise at different stages of the cascade, offering a quality-control perspective on where and why these pipelines can mislead.
Every screening campaign now runs some flavor of co-fold-then-ML-score cascade, and honestly most of us take the ranked list at face value because checking each stage is a pain. That's the exact habit this preprint is poking at, using Keap1-Nrf2 — a notoriously flat, shallow PPI interface that's already brutal for docking and pharmacophore methods — as a stress test where scoring artifacts should show up fast. If the abstract's framing holds, the real message isn't "don't use these tools," it's "don't trust a single composite score without knowing what each stage is actually measuring" — pose confidence, affinity ranking, and physical plausibility are not the same thing, and conflating them is how false positives sneak to the top. Worth revisiting once the full methodology and failure-mode breakdown are out; details here are still thin.