Lorenz VaitlIvan TanasijevicArian Rokkum JamasbL. Santiago Mille-FragosoPan Kessel
Nov 2026
Artificial IntelligenceMachine Learning
Abstract
AlphaFold 3-like models (e.g., Boltz, Protenix, etc.) have pushed the frontier of protein binder design. This architecture is used both for the generation of binder candidates (e.g., by hallucination or converting these models into backbone or full atom generative models) as well as for the screening of these candidates for valid binders. In this paper, we start from the observation that AlphaFold 3's confidence metrics (ipSAE, ipTM) as well as structural consistency metrics can be predicted directly from the trunk even at early recycling. Because our proposed predictor is cheap, differentiable, and available at every cycle, it can be used in two distinct ways: as a decision signal to wisely spend the recycling budget for screening and as an objective for hallucination-based design. We show that both applications lead to increased performance at significantly reduced computational cost.
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