[1]孙连伟,连雪全,杨佳林,等.图神经网络在药物-靶标预测领域的研究进展[J].医学信息,2025,38(08):167-171.[doi:10.3969/j.issn.1006-1959.2025.08.036]
 SUN Lianwei,LIAN Xuequan,YANG Jialin,et al.Research Progress of Graph Neural Network in the Field of Drug-target Prediction[J].Journal of Medical Information,2025,38(08):167-171.[doi:10.3969/j.issn.1006-1959.2025.08.036]
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图神经网络在药物-靶标预测领域的研究进展()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
38卷
期数:
2025年08期
页码:
167-171
栏目:
综述
出版日期:
2025-04-15

文章信息/Info

Title:
Research Progress of Graph Neural Network in the Field of Drug-target Prediction
文章编号:
1006-1959(2025)08-0167-05
作者:
孙连伟连雪全杨佳林李建伟
河北工业大学人工智能与数据科学学院,天津 300401
Author(s):
SUN Lianwei LIAN Xuequan YANG Jialin LI Jianwei
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China
关键词:
药物-靶标预测药物-靶标相互作用药物-靶标亲和力图神经网络异构网络
Keywords:
Drug-target prediction Drug-target interaction Drug-target affinity Graph neural network Heterogeneous network
分类号:
Q811
DOI:
10.3969/j.issn.1006-1959.2025.08.036
文献标志码:
A
摘要:
药物-靶标预测是现代药物研发中至关重要的一个环节,它克服了传统药物研发中发现和验证药物-靶标对的高成本和周期长等问题。药物-靶标相互作用(DTI)预测和药物-靶标亲和力(DTA)预测都是药物-靶标预测的任务。预测药物-靶标相互作用,筛选出具有潜在相互作用的药物-靶标对,对药物重定位以及新药研发具有重要意义。预测药物-靶标亲和力对药物的设计和优化可起到辅助性作用。图神经网络(GNN)作为一种用于处理图结构数据的深度学习模型,它可有效地对同构网络或异构网络中的节点和边进行表示学习,现已被广泛应用于各种药物-靶标预测任务中。本文对图神经网络在药物-靶标预测领域的研究进展作一综述,以期为图神经网络在药物研发领域的进一步发展提供参考。
Abstract:
Drug-target prediction is a crucial part of modern drug research and development. It overcomes the problems of high cost and long cycle in the discovery and verification of drug-target pairs in traditional drug research and development. Drug-target interaction (DTI) prediction and drug-target affinity (DTA) prediction are both tasks of drug-target prediction. Predicting drug-target interactions and screening drug-target pairs with potential interactions are of great significance for drug repositioning and new drug development. Predicting drug-target affinity can play an auxiliary role in drug design and optimization. As a deep learning model for processing graph-structured data, graph neural network (GNN) can effectively represent nodes and edges in homogeneous or heterogeneous networks, and has been widely used in various drug-target prediction tasks. This paper reviews the research progress of graph neural network in the field of drug-target prediction, in order to provide reference for the further development of graph neural network in the field of drug research and development.

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更新日期/Last Update: 1900-01-01