MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs

Guojiang Zhao, Zixiang Lu, Zheng Cheng, Sihang Li, Haitao Lin, Lirong Wu, Yutang Ge, Hanchen Xia, Hengxing Cai, Wentao Guo, Hongshuai Wang, Mingjun Xu, Siyu Zhu, Guolin Ke, Linfeng Zhang, Zhifeng Gao

Keywords: Molecule; Reasoning; Large language model;

Abstract:

Large Language Models (LLMs) have demonstrated remarkable performance
across various domains, yet their capabilities in molecular reasoning remain in-
sufficiently explored. Current approaches tend to rely heavily on general-purpose
prompting, which lacks domain-specific molecular semantics, while those that
use fine-tuning strategies often face challenges with interpretability and reasoning
depth. To address these issues, we introduce MolReasoner, a two-stage frame-
work designed to transition LLMs from memorization towards chemical reason-
ing. First, we propose Mol-SFT, which initializes the model’s reasoning abili-
ties by distilling high-quality reasoning Chain-of-Thought (CoT) trajectories from
GPT-4o, enriched with structural features and functional group information, and
verified for chemical accuracy, enabling the model to internalize coherent and
chemically meaningful reasoning. In the Mol-RL stage, we propose verifiable and
extensible multi-level rewards, where language- and structural-similarity rewards
provide fine-grained semantic and structural alignment. Moreover, we introduce
more comprehensive metrics, together with a multi-dimensional expert-aligned
pipeline to rigorously assess reasoning quality. Extensive experiments demon-
strate that MolReasoner outperforms existing methods, and marking a significant
shift from memorization-based outputs to robust chemical reasoning. The code for
MolReasoner is included in the supplementary materials and will be open-sourced
in the near future.

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

Submission Number: 6596