What question did this study set out to answer?

The central aim is to develop an interpretable model, DrugReasoner, that predicts drug approval outcomes effectively.

February 21, 2026Open Access

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

Key Points

The central aim is to develop an interpretable model, DrugReasoner, that predicts drug approval outcomes effectively.
Developed a reasoning-based large language model (LLM) using the LLaMA architecture.
Fine-tuned the model with group relative policy optimization (GRPO).
Integrated molecular descriptors with comparative reasoning against similar compounds.
Evaluated model performance using AUC and F1 scores on validation and test sets.
Achieved an AUC of 0.732 and F1 score of 0.729 on the validation set.
On the test set, the model reached an AUC of 0.725 and F1 score of 0.718.
Outperformed conventional methods like logistic regression and k-nearest neighbors, and performed competitively compared to XGBoost.
Achieved an AUC of 0.728 and F1 score of 0.774 on an external independent dataset.

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 with an AUC of 0.732 and an F1 score of 0.729 on the validation set and 0.725 and 0.718 on the test set, respectively. These results outperformed conventional baselines, including logistic regression, support vector machine, and k-nearest neighbors and had competitive performance relative to XGBoost. On an external independent dataset, DrugReasoner outperformed both baseline and the recently developed ChemAP model, achieving an AUC of 0.728 and an F1-score of 0.774, while maintaining high precision and balanced sensitivity, demonstrating robustness in real-world scenarios. These findings demonstrate that DrugReasoner not only delivers competitive predictive accuracy but also enhances transparency through its reasoning outputs, thereby addressing a key bottleneck in AI-assisted drug discovery. This study highlights the potential of reasoning-augmented LLMs as interpretable and effective tools for pharmaceutical decision-making.

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