Generalizing Drug Response Prediction through Evidence-Grounded Reasoning

Accurate drug response prediction for previously unseen drugs and cell lines is a central challenge in computational pharmacology. In this setting, a few measured responses provide useful context, but integrating these observations with molecular properties and cellular characteristics remains difficult. We propose a framework for generalizable drug response prediction through evidence-grounded reasoning. The framework uses a large language model to combine molecular and cellular representations with biological facts and a small set of observed responses. To connect these heterogeneous inputs to prediction, we construct chain-of-thought supervision from available drug and cell facts and support-response summaries, without using the query response in reasoning construction. A multi-stage training strategy first aligns biological inputs through response-free pretraining, then initializes evidence-grounded reasoning jointly with response prediction through a teacher-guided warmup. Subsequent numerical prediction updates alternate with reinforcement learning using REINFORCE, where the predictive loss relative to a reference reasoning trace guides the optimization of generated reasoning. A probability-weighted numerical token interface produces the response estimate. This links reasoning to its usefulness for response prediction. At inference, the framework uses the available biological evidence and measured responses to generate a reasoning trace and estimate the response of an unmeasured drug–cell pair. Across cell-blind and drug-blind GDSC2 evaluation with zero or five measurements, the framework records the lowest macro RMSE in the reported comparison, while contextual measurements improve both RMSE and PCC in each protocol.