AlloShift: Finite-Dose Population-Directed Binder Design

Protein function often depends on the populations of different conformational states. Binders can alter this balance by preferentially binding one state, but state preference alone does not determine the effect at a feasible concentration: a selective binder may be too weak to engage the target, while a potent binder may bind both states. We introduce **ALLOSHIFT**, linking state-resolved affinities, baseline populations, and free binder concentration to an exact finite-dose rule for maximizing a desired state or matching a target population. $Q_{bind}$ learns state-resolved affinities from sparse and censored measurements and supplies scores for binder screening and peptide design. Across three measured-affinity panels, affinity-only screening misses the optimum in 70.4% of separating one-binder tasks; the balance of potency and selectivity changes with dose. $Q_{bind}$ improves receptor-cold affinity and state-direction prediction, and its scores improve model-evaluated peptide guidance and reranking. Together, these results provide a population-aware design objective and a learned route to candidate prioritization.