DrugReasoner：基於推理增強語言模型的可解釋藥物審批預測系統

摘要

藥物發現是一個複雜且資源密集的過程，因此早期預測批准結果對於優化研究投資至關重要。雖然傳統的機器學習和深度學習方法在藥物批准預測方面顯示出潛力，但其有限的解釋性限制了其影響力。在此，我們介紹了DrugReasoner，這是一個基於LLaMA架構並通過群組相對策略優化（GRPO）微調的推理型大型語言模型（LLM），用於預測小分子藥物獲批的可能性。DrugReasoner整合了分子描述符，並通過與結構相似的已批准和未批准化合物進行比較推理，生成預測結果，同時提供逐步推理過程和置信度評分。DrugReasoner在驗證集上取得了穩健的性能，AUC為0.732，F1分數為0.729；在測試集上分別為0.725和0.718。這些結果超越了包括邏輯回歸、支持向量機和k近鄰在內的傳統基線模型，並與XGBoost相比具有競爭力。在一個外部獨立數據集上，DrugReasoner超越了基線模型和最近開發的ChemAP模型，AUC達到0.728，F1分數為0.774，同時保持了高精度和平衡的敏感性，展示了其在真實場景中的穩健性。這些發現表明，DrugReasoner不僅提供了具有競爭力的預測準確性，還通過其推理輸出增強了透明度，從而解決了AI輔助藥物發現中的一個關鍵瓶頸。本研究強調了推理增強型LLM作為可解釋且有效的藥物決策工具的潛力。

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：基於推理增強語言模型的可解釋藥物審批預測系統

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

摘要

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