基准测试 基于序列的化合物-蛋白质相互作用预测通过构建一个偏差数据集 CDPNN
Yang Hao1,2,3,4, Bo Li2,3, Daiyun Huang2,5
1Department of Hepatobiliary Surgery, Haikou Affiliated Hospital of Central South University Xiangya School of Medicine, Haikou 570208, P.R. China.
Journal of chemical information and modeling
|November 20, 2025
概括
准确的化合物-蛋白相互作用 (CPI) 预测对于药物发现至关重要. 一种新方法,CDPN,可对数据集进行微分析,以提高机器学习模型的概括性和虚拟选性能.
科学领域:
- 计算化学是一种计算化学.
- 药物发现 药物发现
- 机器学习是机器学习.
背景情况:
- 对化合物-蛋白相互作用 (CPI) 的准确预测对于加速药物发现至关重要.
- 现有的数据集往往含有偏差,例如过度代表的分子支架和不平衡的标签分布,这可能导致机器学习快捷方式并阻碍模型通用化.
- 目前的退化方法可能会损害数据集的多样性.
研究的目的:
- 为了解决化合物-蛋白相互作用 (CPI) 数据集中的偏差.
- 引入一个新的协议,以集群为基础的下方采样和假定负值 (CDPN),用于构建一个被删除的CPI基准.
- 通过使用CDPN数据集,系统地对基于深度学习的CPI模型,特别是蛋白质语言模型进行基准比较.
主要方法:
- 开发了基于集群的下方采样和假定负数 (CDPN) 协议.
- 通过复合集群级下方采样,CDPN可以减轻偏差.
- 从未开发的化学空间生成假定负面,以确保标签的均衡分配.
主要成果:
- 在CDPN数据集上系统比较深度学习CPI模型.
- 在PDBbind.com上发现了蛋白质语言模型的注意力解释能力的局限性.
- 发现KPGT-Ankh是一个优越的模型,通过对CDPN数据集的废除研究,增强了概括性和虚拟查性能.
结论:
- 实际上,CDPN协议可以创建一个非数据化的CPI基准.
- 在CPI预测方面,KPGT-Ankh表现出卓越的表现.
- 高性能模型被集成到DeepSEQreen,一个无代码的Web服务器,以提高可访问性和促进社区反药物发现研究.
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