Knowledge mapping of graph neural networks for drug discovery: a bibliometric and visualized analysis

作者：Rufan Yao, Zhenhua Shen, Xinyi Xu, Guixia Ling, Rongwu Xiang, Tingyan Song, Fei Zhai, Yuxuan Zhai · 发表于：Frontiers in Pharmacology · 年份：2024 · DOI：10.3389/fphar.2024.1393415 · 被引用次数：28 · 研究领域：Computational Drug Discovery Methods、Advanced Graph Neural Networks、Advanced Technologies in Various Fields

Introduction: In recent years, graph neural network has been extensively applied to drug discovery research. Although researchers have made significant progress in this field, there is less research on bibliometrics. The purpose of this study is to conduct a comprehensive bibliometric analysis of graph neural network applications in drug discovery in order to identify current research hotspots and trends, as well as serve as a reference for future research. Methods: Publications from 2017 to 2023 about the application of graph neural network in drug discovery were collected from the Web of Science Core Collection. Bibliometrix, VOSviewer, and Citespace were mainly used for bibliometric studies. Results and Discussion: In this paper, a total of 652 papers from 48 countries/regions were included. Research interest in this field is continuously increasing. China and the United States have a significant advantage in terms of funding, the number of publications, and collaborations with other institutions and countries. Although some cooperation networks have been formed in this field, extensive worldwide cooperation still needs to be strengthened. The results of the keyword analysis clarified that graph neural network has primarily been applied to drug-target interaction, drug repurposing, and drug-drug interaction, while graph convolutional neural network and its related optimization methods are currently the core algorithms in this field. Data availability and ethical supervisio...