Prediction-Reasoning Fusion for Molecular Hit Discovery

Molecular hit discovery requires turning imperfect activity predictions into useful assay choices. We study prediction-reasoning fusion, in which a predictive model estimates molecular activity, a language model proposes interpretable acquisition policies, and a policy evaluator measures their utility on development data. The language model requests numerical summaries relating predictions and molecular features to observed outcomes, proposes decision trees and uses evaluation feedback to guide subsequent proposals. A tree guides the next assay batch only when it improves development utility over model-based ranking. Before each subsequent acquisition round, the predictive model is refitted using the initial and acquired labels, and a new tree-development session begins. This automated, agentic procedure produces decision rules that scientists can examine alongside their development support. A theoretical bound relates the transfer of development gains to adaptive data reuse, differences between candidate populations and the extent of policy changes. Experiments on HIV and ALDH1 show higher mean acquisition hits and early discovery than ranking by a multilayer perceptron ensemble and random-tree search, although higher development scores do not always lead to better acquisition outcomes. A supplementary exploratory held-out assessment finds significant hit gains on HIV.