DHAG-DTA:用于药物标结合亲和力预测的动态层次亲和力图模型
IEEE transactions on computational biology and bioinformatics
|August 14, 2025
概括
我们开发了DHAG-DTA,一个深层神经网络,使用分子序列数据预测药物标结合亲和力. 这种方法实现了最先进的性能,提高了药物发现效率.
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:
- 药物标结合亲和力 (DTA) 的预测对于识别潜在的治疗方法至关重要.
- 深度神经网络 (DNN) 对DTA预测具有前景,特别是只有序列数据可用时.
研究的目的:
- 提出DHAG-DTA,一种用于DTA预测的新型动态层次亲和度图DNN方法.
- 利用分子序列信息和已知的药物向相互作用来提高预测准确度.
主要方法:
- DHAG-DTA使用了两层层次的层次图:用于分子间相互作用的亲和力图和用于分子内相互作用的嵌入式分子图.
- 关键的创新包括统一的层次图,动态亲和图结构确定,跳过连接信息融合,和强大的功能嵌入未见的分子.
主要成果:
- 与现有模型相比,DHAG-DTA在两个基准数据集上表现出更高的性能.
- 该模型在多个评估指标上取得了最先进的结果.
结论:
- DHAG-DTA有效地整合了分子间和分子内相互作用信息,以准确地预测DTA.
- 拟议的方法在药物发现和开发中推进了计算方法.
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