利用大型语言模型,以文学为基础,对蛋白质结合口袋进行优先排序
Roman Stratiichuk1,2, Mykola Melnychenko1, Ihor Koleiev1,3
1Receptor.AI Inc., London N1 7GU, United Kingdom.
Bioinformatics (Oxford, England)
|August 12, 2025
概括
这项研究引入了一种新方法,将几何口袋检测和大型语言模型 (LLM) 结合起来,用于识别药物发现的蛋白质结合口袋. 这种方法使残留物提取自动化,克服了手工瓶.
科学领域:
- 计算化学是一种计算化学.
- 生物信息学是一种生物信息学.
- 药物发现 药物发现
背景情况:
- 识别蛋白质结合口袋对于小分子药物发现至关重要.
- 目前用于口袋识别的手动方法耗时,易出错,且效率低下.
- 手动修复结合部位数据阻碍了高通量药物发现.
研究的目的:
- 开发一种新的,自动化的方法来识别和优先考虑蛋白质结合口袋.
- 利用大型语言模型 (LLM) 来从科学文献中提取残留级信息.
- 提高小分子药物发现管道的效率和准确性.
主要方法:
- 结合几何口袋检测 (Fpocket) 与LLM用于口袋识别和验证.
- 利用LLM与微调提示,从已公布的实验数据中提取残留信息.
- 开发并使用一个精心策划的基准数据集用于培训和评估LLM绩效.
主要成果:
- 通过使用LLMs成功验证了与已发表的实验数据相对应的候选人口袋.
- 证明了LLM在评估论文相关性和提取关键口袋信息方面的能力.
- 建立了一种用于蛋白质结合口袋识别的新型自动化工作流程.
结论:
- 结合几何和LLM方法在自动化蛋白质结合口袋识别方面取得了重大进展.
- 这种方法解决了手工治疗的局限性,加速了药物发现.
- 开发的数据集和方法公开提供,以促进进一步的研究.
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