通过多模式对比学习提高蛋白质 - 连接物结合亲和力预测的概括性
Ding Luo1, Dandan Liu1, Xiaoyang Qu2,3
1State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.
Journal of chemical information and modeling
|March 5, 2024
概括
这项研究引入了一种新的图形神经网络评分函数,使用三重对比学习来增强蛋白质-连接体结合亲缘关系预测. 新模型显示了改进的概括性,有助于药物发现.
科学领域:
- 计算化学计算化学
- 结构生物学 结构生物学
- 药物发现 药物发现 药物发现
背景情况:
- 准确预测蛋白质 - 配体结合亲和力对于药物发现至关重要.
- 当前的机器学习方法往往因为相互作用的单模表示而难以概括.
研究的目的:
- 开发基于图形神经网络的评分函数,以提高蛋白质 - 配体结合性亲缘关系预测中的概括能力.
- 通过多模式表示来增强对蛋白质 - 配体相互作用的全面理解.
主要方法:
- 利用图形神经网络架构进行评分功能开发.
- 实现了三重对比的学习损失函数.
- 集成的三维复杂表示与融合的二维连接体和粗粒度的口袋表示.
主要成果:
- 拟议的模型在多个外部数据集上展示了卓越的概括能力.
- 与现有的基于深度学习的评分功能相比,实现了更好的性能.
- 验证了多模式表示和对比学习方法的有效性.
结论:
- 开发的评分功能显示了作为药物发现工具的重大前景.
- 该培训框架可适应其他生物物理和生物化学预测任务,例如蛋白质-蛋白质相互作用和突变效应.
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