BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation

• URL: http://arxiv.org/abs/2512.04629v2
• Date: Thu, 11 Dec 2025 08:24:32 GMT
• Title: BioMedGPT-Mol: Multi-task Learning for Molecular Understanding and Generation
• Authors: Chenyang Zuo, Siqi Fan, Zaiqing Nie,
• Abstract summary: We introduce BioMedGPT-Mol, a molecular language model designed to support molecular understanding and generation tasks.<n>By curating and unifying existing public instruction datasets, we have assembled a large-scale, comprehensive, and high-quality training dataset.<n>The model is then fine-tuned through a meticulously designed multi-task learning framework.
• Score: 9.078742514163524
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Molecules play a crucial role in biomedical research and discovery, particularly in the field of small molecule drug development. Given the rapid advancements in large language models, especially the recent emergence of reasoning models, it is natural to explore how a general-purpose language model can be efficiently adapted for molecular science applications. In this work, we introduce BioMedGPT-Mol, a molecular language model designed to support molecular understanding and generation tasks. By curating and unifying existing public instruction datasets, we have assembled a large-scale, comprehensive, and high-quality training dataset. The model is then fine-tuned through a meticulously designed multi-task learning framework. On a consolidated benchmark derived from LlaSMol, TOMG-Bench, and MuMOInstruct, BioMedGPT-Mol achieves remarkable performance. Our experimental results demonstrate that a general-purpose reasoning model can be effectively and efficiently post-trained into a professional molecular language model through a well-structured multi-task curriculum. Leveraging these capabilities, we further apply the model to multi-step retrosynthetic planning, achieving state-of-the-art performance on RetroBench and demonstrating its superior efficacy as an end-to-end retrosynthetic planner. We anticipate that our approach can be extended to other biomedical scientific domains.

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