MMSG-DTA:一种基于序列和图形模式的多模式,多尺度模型,用于药物向 afinity 预测.
Jiahao Xu1,2, Lei Ci1, Bo Zhu1
1School of Information Engineering, Huzhou University, Huzhou 313000, China.
Journal of chemical information and modeling
|January 8, 2025
概括
这项研究引入了MMSG-DTA,这是一种用于药物向亲和力 (DTA) 预测的新型模型. 它通过整合分子图和蛋白质序列数据来提高准确性,以获得更好的药物发现见解.
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:
- 药物标亲和力 (DTA) 预测对于药物发现至关重要,但目前的方法与分子图和蛋白质特征提取斗争.
- 现有的模型往往无法捕捉全球分子图的特征,并且依赖于有限的1D蛋白序列,阻碍了准确的相互作用建模.
研究的目的:
- 开发一种先进的模型,MMSG-DTA,用于改进药物目标亲和力预测.
- 为了解决捕获全球分子图形特征的局限性,并增强蛋白质表示,以便更好地分析药物向相互作用.
主要方法:
- 提出了一种多模式,多尺度模型 (MMSG-DTA),将图形神经网络和变压器结合起来,用于分子图形特征提取.
- 利用基于图形的模式来改进从氨基酸序列中提取蛋白质特征.
- 整合了基于注意力的特征融合模块,以集成各种特征类型,以增强表现能力.
主要成果:
- 在三个基准数据集 (戴维斯,KIBA,梅茨) 中,MMSG-DTA在DTA预测方面表现出卓越的表现.
- 该模型有效地从分子图表中捕获了本地和全球特征,并改进了蛋白质表示.
- 实验结果显示,MMSG-DTA的性能优于一些最先进的DTA预测方法.
结论:
- 拟议的MMSG-DTA模型显著提高了药物向相互作用预测的准确性和稳定性.
- 多模式和多尺度方法有效地整合了各种数据模式,用于全面的特征学习.
- 这项工作提供了一个强大的工具,通过增强的DTA预测来加速药物发现和开发.
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