AlloGen: Conformation-Selective Binder Design with Differential State Scoring

Protein binder design has largely optimized for affinity alone, leaving conformational selectivity unaddressed: for allosteric targets such as kinases, nuclear receptors, and GPCRs, a binder that engages both active and inactive states provides no functional specificity regardless of how tightly it binds. We introduce **AlloGen**, a modular framework that decouples backbone generation from a learned state-selectivity scorer $Q_\theta$, an SE(3)-invariant interface graph transformer trained via a two-phase curriculum that first grounds interface geometry before imposing conformational discrimination. Because $Q_\theta$ is fully differentiable and generator-agnostic, it integrates with any backbone generator as a passive reranker or an active gradient-based guide without retraining. Trained on 65 targets spanning 15 protein families, $Q_\theta$ generalizes to held-out out-of-distribution targets where energy-based baselines fail entirely, and all 15 evaluated generator--guidance combinations achieve positive conformational selectivity averaged over the held-out targets, with the best reaching $\bar{S}=+0.677$. Our anonymous code repository can be found at https://anonymous.4open.science/r/AlloGen_NeurIPS-04CB.