DrugReasoner: Interpretable drug approval prediction with a reasoning-augmented language model.

ABSTRACT: Drug discovery is a complex and resource-intensive process, making early prediction of approval outcomes critical for optimizing research investments. While classical machine learning and deep learning methods have shown promise in drug approval prediction, their limited interpretability constraints their impact. Here, we present DrugReasoner, a reasoning-based large language model (LLM) built on the LLaMA architecture and fine-tuned with group relative policy optimization (GRPO) to predict the likelihood of small-molecule approval. DrugReasoner integrates molecular descriptors with comparative reasoning against structurally similar approved and unapproved compounds, generating predictions alongside step-by-step rationales and confidence scores. DrugReasoner achieved robust performance wit

SUBMITTER: Ghaffarzadeh-Esfahani M 

PROVIDER: S-EPMC12919791 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature