DrugReasoner: Interpretable Drug Approval Prediction with a
  Reasoning-augmented Language Model
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.
68aef2695f5ef03314d8c4aa
Mohammadreza Ghaffarzadeh-Esfahani
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2025-08-27T15:00:48.492Z
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MoRezaGH
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Moreza009
68aef2695f5ef03314d8c4ab
Ali Motahharynia
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2025-08-27T14:59:22.333Z
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Ali Motahharynia
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alimotahharynia
68aef2695f5ef03314d8c4ac
Nahid Yousefian
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2025-08-27T14:59:04.711Z
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Nahid Yousefian
user
nahidysf
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Navid Mazrouei
68aef2695f5ef03314d8c4ae
Jafar Ghaisari
68aef2695f5ef03314d8c4af
Yousof Gheisari
DrugReasoner: Interpretable Drug Approval Prediction with a
  Reasoning-augmented Language Model
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, a reasoning-based large language model fine-tuned with GRPO, predicts small-molecule approval with high accuracy and interpretability, outperforming conventional methods and enhancing transparency in drug discovery.
large language model LLaMA architecture group relative policy optimization molecular descriptors comparative reasoning AUC F1 score logistic regression support vector machine k-nearest neighbors XGBoost ChemAP model precision sensitivity reasoning-augmented LLMs