Hybrid AI/physics pipeline for miniprotein binder prioritization: application to the BRD3 ET domain

Abstract

AI-based protein design can rapidly generate thousands of candidate binders, but most fail to fold or bind productively, creating a critical need for robust prioritization. We present a generalizable hybrid pipeline that integrates deep-learning design and physics-based simulations to filter large libraries down to a handful of high-confidence candidates.