注意MGT-DTA:使用图形变换器和注意力机制进行多模式药物向亲和度预测
Hongjie Wu1, Junkai Liu2, Tengsheng Jiang3
1School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China.
这项研究介绍了AttentionMGT-DTA,这是一个新的深度学习模型,用于预测药物标亲和力 (DTA). 该模型通过准确地表示分子和结合口袋图来增强药物发现,优于现有方法.
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:
- 准确的药物向亲和力 (DTA) 预测对于有效的药物发现至关重要,但传统的实验方法昂贵且耗时.
- 深度学习模型对DTA预测有希望,但在有效的药物和目标表示和捕获交互信息方面存在困难.
研究的目的:
- 开发一个先进的深度学习模型, AttentionMGT-DTA,用于准确的药物向亲和力预测.
- 改善药物和目标的表现,并加强在DTA预测中捕获相互作用信息.
主要方法:
- 建议AttentionMGT-DTA,一个基于注意力的多模式深度学习模型.
- 使用分子图表表示药物,使用绑定口袋图表表示目标.
- 采用了两个注意力机制,以整合跨药物标对和蛋白质模式的信息.
主要成果:
- 注意MGT-DTA在两个基准数据集上显著超过了最先进的基线模型.
- 该模型通过可视化药物原子和蛋白质残留之间的相互作用来证明了高解释性.
结论:
- 注意MGT-DTA为药物向 afinity 预测提供了一种强大且可解释的方法.
- 该模型在药物发现和设计中推进了深度学习应用.
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