将域名知识与微调的大型语言模型融合在一起,用于增强分子性质预测
Liangxu Xie1, Yingdi Jin2, Lei Xu1
1Institute of Bioinformatics and Medical Engineering, Jiangsu University of Technology, Changzhou 213001, China.
Journal of chemical theory and computation
|July 9, 2025
概括
本研究引入了双模学习 (KFLM2) 的知识融合大语言模型,以增强药物发现中的分子性质预测. 将领域知识与LLM集成,提高了准确性,可能会彻底改变药物开发.
科学领域:
- 计算化学的计算化学
- 药物发现 药物发现 药物发现
- 人工智能的人工智能
背景情况:
- 大型语言模型 (LLM) 在科学应用中表现有前途,但在分子性质预测方面存在困难.
- 现有的化学专用LLM在这个关键的药物发现任务中没有取得令人满意的表现.
研究的目的:
- 通过将深入的领域知识整合到法学士课程中,提高分子性质预测的准确性.
- 开发一种新的双模学习方法,以改善药物发现预测.
主要方法:
- 微调的DeepSeek-R1-Distill-Qwen-1.5B使用ZINC和ChEMBL数据集来获得SMILES嵌入.
- 集成的LLM衍生SMILES嵌入式与分子图表表示.
- 训练了一种混合神经网络,使用组合双模态输入来进行财产预测.
主要成果:
- 双模式的知识融合大语言模型 (KFLM2) 在十个回归和分类数据集中的九个实现了更高的预测性能.
- 视觉化证实,将LLM嵌入与分子图的结合提供了互补信息,提高了预测准确度.
- 模型的性能不仅取决于尺寸,还取决于预训练和微调的有效知识整合.
结论:
- 将领域知识集成到LLM中是一种合理和有效的策略,用于精确的分子性质预测.
- 拟议的KFLM2方法为彻底改变药物开发和发现过程提供了重大进展.
- 双模学习结合了LLM嵌入和分子图表,增强了超越单模方法的预测能力.
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