{"ID":2857143,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.10248","arxiv_id":"2510.10248","title":"Reasoning-Enhanced Large Language Models for Molecular Property Prediction","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.","short_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 molecula...","url_abs":"https://arxiv.org/abs/2510.10248","url_pdf":"https://arxiv.org/pdf/2510.10248v2","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\"]","published":"2025-10-11T15:05:45Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Reinforcement Learning\",\"Language Model\"]","project_urls":"[\"https://anonymous.4open.science/r/MPPReasoner-12687\"]","has_code":false}