TransVAE-DTA:转换器和变异自编码器网络用于药物标结合亲缘关系预测.
Changjian Zhou1, Zhongzheng Li2, Jia Song3
1School of life sciences, Northeast Agricultural University, Harbin, PR China; Department of Data and Computing, Northeast Agricultural University, Harbin, PR China.
这项研究引入了TransVAE-DTA,这是一个用于药物标结合亲缘关系预测的新框架. 它通过有效模拟蛋白质长距离关系和药物向相互作用来增强药物发现.
科学领域:
- 计算机化药物发现.
- 生物信息学是一种生物信息学.
- 在药理学中的机器学习.
背景情况:
- 计算的药物标结合亲和力 (DTA) 预测对于药物发现和重新定位至关重要.
- 现有的方法与蛋白质长距离关系和简单的相互作用建模扎.
研究的目的:
- 为了解决当前DTA预测方法的局限性.
- 为改进DTA预测提出一个新的框架,TransVAE-DTA.
主要方法:
- 结合了用于目标表示的变压器架构和用于药物结构编码的变异自编码器 (VAE).
- 引入了一个适应性注意力聚合 (AAP) 模块,用于特征融合.
- 最大化了药物,目标及其DTA的联合概率的下界.
主要成果:
- 在DTA预测方面,TransVAE-DTA表现出卓越的性能.
- 对公开的戴维斯和KIBA数据集进行验证.
结论:
- TransVAE-DTA提供了一种新的方法来设计药物向相互作用.
- 该框架推进了计算药物发现领域.
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