DrugReasoner：基于推理增强语言模型的可解释药物审批预测

摘要

药物研发是一个复杂且资源密集的过程，早期预测审批结果对于优化研究投资至关重要。尽管传统的机器学习和深度学习方法在药物审批预测中展现出潜力，但其有限的解释性制约了其影响力。本文提出了DrugReasoner，一个基于推理的大型语言模型（LLM），构建于LLaMA架构之上，并通过群体相对策略优化（GRPO）进行微调，用于预测小分子药物获批的可能性。DrugReasoner整合了分子描述符，并与结构相似的已获批和未获批化合物进行对比推理，生成预测结果的同时提供逐步推理过程和置信度评分。在验证集上，DrugReasoner取得了稳健的性能，AUC为0.732，F1得分为0.729；在测试集上，AUC和F1得分分别为0.725和0.718，这些结果超越了包括逻辑回归、支持向量机和k近邻在内的传统基线方法，并与XGBoost相比具有竞争力。在一个外部独立数据集上，DrugReasoner不仅超越了基线模型，还超越了近期开发的ChemAP模型，实现了AUC为0.728和F1得分为0.774，同时保持了高精度和平衡的敏感性，展现了在实际应用中的鲁棒性。这些发现表明，DrugReasoner不仅提供了具有竞争力的预测准确性，还通过其推理输出增强了透明度，从而解决了AI辅助药物发现中的一个关键瓶颈。本研究强调了推理增强型LLM作为可解释且有效的药物决策工具的潜力。

English

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.

DrugReasoner：基于推理增强语言模型的可解释药物审批预测

DrugReasoner: Interpretable Drug Approval Prediction with a Reasoning-augmented Language Model

摘要

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