KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge

• URL: http://arxiv.org/abs/2510.19484v1
• Date: Wed, 22 Oct 2025 11:23:58 GMT
• Title: KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
• Authors: Zaifei Yang, Hong Chang, Ruibing Hou, Shiguang Shan, Xilin Chen,
• Abstract summary: We introduce KnowMol-100K, a large-scale dataset with 100K fine-grained molecular annotations across multiple levels.<n>We also propose chemically-informative molecular representation, effectively addressing limitations in existing molecular representation strategies.<n>KnowMol achieves superior performance across molecular understanding and generation tasks.
• Score: 73.51130155601824
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The molecular large language models have garnered widespread attention due to their promising potential on molecular applications. However, current molecular large language models face significant limitations in understanding molecules due to inadequate textual descriptions and suboptimal molecular representation strategies during pretraining. To address these challenges, we introduce KnowMol-100K, a large-scale dataset with 100K fine-grained molecular annotations across multiple levels, bridging the gap between molecules and textual descriptions. Additionally, we propose chemically-informative molecular representation, effectively addressing limitations in existing molecular representation strategies. Building upon these innovations, we develop KnowMol, a state-of-the-art multi-modal molecular large language model. Extensive experiments demonstrate that KnowMol achieves superior performance across molecular understanding and generation tasks. GitHub: https://github.com/yzf-code/KnowMol Huggingface: https://hf.co/datasets/yzf1102/KnowMol-100K

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