基于多头注意力GAN的药物靶点亲和力预测研究进展与展望-国际医学与数据杂志-CSCIED科技核心评价数据库-手机版

基于多头注意力GAN的药物靶点亲和力预测研究进展与展望

Research progress and prospects of drug target affinity prediction based on multi head attention GAN

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DOI	10.12208/j.ijmd.20250017
刊名	国际医学与数据杂志 International Journal of Medicine and Data
年，卷(期)	2025, 9(1)
作者	徐璐洋*,谢跃生,岳中乐,陈彦君,孟凡爱,陈果,
作者单位	华北理工大学河北唐山
摘要	近年来深度学习方法在小分子药物与蛋白质靶标的亲和力（DTA）预测领域取得了显著进展，生成对抗网络（GAN）已被应用于该领域，但当前主流方法多局限于浅层GAN架构。本文综述了基于深度学习的DTA预测方法，探讨了其在提高预测精度和模型可解释性方面的潜力。文章首先回顾了传统方法和深度学习方法的优缺点，随后重点介绍了多头注意力机制和GAN的结合应用，最后总结了当前研究的挑战和未来发展方向。
Abstract	Recent advances in deep learning have significantly improved small molecule-drug target affinity (DTA) prediction, with generative adversarial networks (GANs) emerging as a promising approach. However, current GAN-based methods primarily use shallow architectures. This paper reviews deep learning-based DTA prediction methods, examining their potential to enhance accuracy and interpretability. We compare traditional and deep learning approaches, highlight the integration of multi-head attention with GANs, and discuss current challenges and future directions.
关键词	DTA预测；生成对抗网络；Attention机制；SHAP
KeyWord	DTA prediction; Generate adversarial networks; Attention mechanism; SHAP
基金项目
页码	84-87

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引用本文

徐璐洋*,谢跃生,岳中乐,陈彦君,孟凡爱,陈果. 基于多头注意力GAN的药物靶点亲和力预测研究进展与展望 [J]. 国际医学与数据杂志. 2025; 9; (1). 84 - 87.

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