MERLE: Many-Objective Extrapolative Search via Representation Distillation for Biological Landscape Explainability

Biological landscape search balances many objectives with limited measurements. Restricted initial observations demand extrapolation under substantial predictive uncertainty. We study sequential many-objective search where each query reveals a complete response profile, aiming to expand the observed Pareto frontier under a fixed measurement budget. We introduce MERLE, which distills pretrained mutation preferences into interpretable, objective-specific priors and combines them with an adaptive position-level response model. Together they guide frontier search and reveal which positions matter to the campaign. At each position, the model pools substitutions to estimate assay effects and uncertainty. MERLE scores candidates against unmet frontier requirements and allocates queries across teacher-guided, position-only, and embedding-coverage proposals based on observed frontier gains. Across diverse landscapes and altered campaign settings, MERLE improves frontier discovery over competitive baselines. Ablations identify when the teacher and position field help; site deletion and independent-assay measurements link frontier gains to consequential, reproducible choices of mutation positions across laboratories and distinct antibody and serum panels studied.