DrugReasoner: Interpretable Drug Approval Prediction with a
  Reasoning-augmented Language Model
Authors: name: Mohammadreza Ghaffarzadeh-Esfahani, name: Ali Motahharynia, name: Nahid Yousefian, name: Navid Mazrouei, name: Jafar Ghaisari, name: Yousof Gheisari

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.