RESOLVE: Jointly Valuing Model Adaptation and Experiments for Protein Engineering

Protein engineering requires choosing which variants to measure and how to adapt predictive models from limited experimental data. Nonmyopic experimental design values measurements for both immediate discovery and their ability to improve later choices. When models are fine-tuned on new observations, these choices also depend on changing representations. We introduce RESOLVE, a controller that combines acquisition lookahead with a common discovery objective for model adaptation and experimental selection. Its evaluator accounts for representation changes through hypothetical predictor and representation updates and assesses frozen and adapted recommendations through the experimental batches they propose. Our theoretical analysis connects valuation accuracy to immediate selection quality. Retrospective experiments across diverse protein assays and feedback schedules demonstrate improved discovery over acquisition and planning baselines under matched measurement budgets. Ablation studies show that accounting for adapted representations improves value assessment, while selecting between frozen and adapted recommendations improves discovery. These results support representation-aware valuation and coupled action-batch selection as useful components of nonmyopic experimental design for protein engineering.