Reasoning-Enhanced Large Language Models for Molecular Property Prediction

• URL: http://arxiv.org/abs/2510.10248v2
• Date: Fri, 17 Oct 2025 13:23:43 GMT
• Title: Reasoning-Enhanced Large Language Models for Molecular Property Prediction
• Authors: Jiaxi Zhuang, Yaorui Shi, Jue Hou, Yunong He, Mingwei Ye, Mingjun Xu, Yuming Su, Linfeng Zhang, Ying Qian, Linfeng Zhang, Guolin Ke, Hengxing Cai,
• Abstract summary: Molecular property prediction is crucial for drug discovery and materials science.<n>Existing approaches suffer from limited interpretability, poor cross-task generalization, and lack of chemical reasoning capabilities.<n>We propose MPPReasoner, a multimodal large language model that incorporates chemical reasoning for molecular property prediction.
• Score: 19.593493317167646
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecular property prediction is crucial for drug discovery and materials science, yet existing approaches suffer from limited interpretability, poor cross-task generalization, and lack of chemical reasoning capabilities. Traditional machine learning models struggle with task transferability, while specialized molecular language models provide little insight into their decision-making processes. To address these limitations, we propose \textbf{MPPReasoner}, a multimodal large language model that incorporates chemical reasoning for molecular property prediction. Our approach, built upon Qwen2.5-VL-7B-Instruct, integrates molecular images with SMILES strings to enable comprehensive molecular understanding. We develop a two-stage training strategy: supervised fine-tuning (SFT) using 16,000 high-quality reasoning trajectories generated through expert knowledge and multiple teacher models, followed by Reinforcement Learning from Principle-Guided Rewards (RLPGR). RLPGR employs verifiable, rule-based rewards that systematically evaluate chemical principle application, molecular structure analysis, and logical consistency through computational verification. Extensive experiments across 8 datasets demonstrate significant performance improvements, with MPPReasoner outperforming the best baselines by 7.91\% and 4.53\% on in-distribution and out-of-distribution tasks respectively. MPPReasoner exhibits exceptional cross-task generalization and generates chemically sound reasoning paths that provide valuable insights into molecular property analysis, substantially enhancing both interpretability and practical utility for chemists. Code is available at https://anonymous.4open.science/r/MPPReasoner-12687.

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