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GramSeq-DTA:一个基于语法的药物向亲和力预测方法,融合基因表达信息

Kusal Debnath¹, Pratip Rana², Preetam Ghosh¹

¹Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA.

|March 28, 2025

概括

GramSeq-DTA通过将药物化学扰动数据与结构特征集成来提高药物向亲和力 (DTA) 的预测. 这种新的方法提高了药物发现现有方法的准确性.

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.
当前的1D字符串和基于图形的表示在捕获全面的药物和目标信息方面存在局限性.
整合功能遗传信息可以提高DTA预测的准确性.

研究的目的:

提出GramSeq-DTA,一种新的方法,将化学扰动数据与结构信息相结合,用于增强DTA预测.
通过结合功能遗传洞察来解决现有的DTA预测模型的局限性.

主要方法:

使用语法变异自编码器 (GVAE) 来提取药物特征.
使用卷积神经网络 (CNN) 和循环神经网络 (RNN) 来提取蛋白质特征.
综合药物结构特征,来自L1000项目的基因表达数据 (化学扰动) 和目标特征.

主要成果:

拟议的GramSeq-DTA模型在与最先进的DTA预测方法相比显示出更高的性能.
对BindingDB,Davis和KIBA数据集的验证证实了该模型的有效性.
整合结构和功能 (化学扰动) 数据显著提高了预测准确性.

结论:

GramSeq-DTA通过结合结构和功能生物学数据,为DTA预测提供了一种新而实用的方法.
这种多模式方法提高了DTA预测的准确性,并鼓励进一步研究药物发现的综合特征表示.

关键词:

化学扰动是一种化学扰动.深度学习是一种深度学习.药物目标亲和力基于语法编码的编码.多式联运是多式联运.

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