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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[1] Zhang D, Zhang T, Zhu J, et al. Discovery of novel targets and me chanisms of MEK inhibitor Selumetinib for LGG treatment based on molecular docking and molecular dynamics simulation
[J]. Journal of Molecular Modeling, 2022, 28(6): 138.
[2] Pahikkala T, Airola A, Pietilä S, et al. Toward more realistic drug–target interaction predictions
[J]. Briefings in bioinformatics, 2015, 16(2): 325-337.
[3] Zhu Z, Yao Z, Zheng X, Qi G, Li Y, Mazur N, et al. Drug–target affinity prediction method based on multi-scale information interaction and graph optimization. Comput
[J]. Biol, 2023, Med. 167.
[4] Thin Nguyen, Hang Le, Thomas P Quinn, Tri Nguyen, Thuc Duy Le, Svetha Venkatesh, GraphDTA: predicting drug–target binding affinity with graph neural networks, Bioinformatics, Volume 37, Issue 8, March 2021, Pages 1140–1147.
[5] 彭杨,朱小飞,胡冬冬.基于注意力感知和模态融合的药物-靶点相互作用预测方法
[J/OL].太原理工大学学报:1-10
[2024-04-23].

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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