Reasoning-Enhanced Large Language Models for Molecular Property Prediction

ICLR 2026 Conference Submission12687 Authors

Keywords: Molecular Property Prediction, Chemical Reasoning, Drug Discovery, Large Language Model, Explainable AI

TL;DR: We introduce MPPReasoner, a multimodal LLM that incorporates chemical reasoning for molecular property prediction through principle-guided reinforcement learning.

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.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 12687