用于药物发现的图形神经网络的知识映射:一个文献计量和可视化分析
Rufan Yao1, Zhenhua Shen1, Xinyi Xu1
1Faculty of Medical Device, Shenyang Pharmaceutical University, Shenyang, China.
Frontiers in pharmacology
|May 27, 2024
概括
这项文献分析显示,图形神经网络在药物发现中越来越重要,特别是在药物向相互作用方面. 关键的挑战包括数据,可解释性和全球合作,以促进AI在医学中的发展.
科学领域:
- *计算化学和化学信息学.
- * 药学研究中的人工智能.
背景情况:
- *图形神经网络 (GNN) 在药物发现方面显示出重大前景.
- * 在药物发现中GNN应用的文献分析尚未得到充分研究.
- * 本研究提供了GNN在药物发现中的综合文献统计概述.
研究的目的:
- 在药物发现中对GNN应用进行全面的文献计量分析.
- 确定当前的研究热点和新兴趋势.
- 为未来的研究方向和合作提供参考.
主要方法:
- 通过Web of Science核心集合收集了2017-2023年间的652个出版物.
- 使用了包括Bibliometrix,VOSviewer和Citespace在内的文献计量工具.
- 分析了出版物数据,国家贡献,合作和关键字频率.
主要成果:
- 对用于药物发现的GNN的研究兴趣正在迅速增长.
- 中国和美国在出版物,资金和合作方面处于领先地位.
- 关键应用包括药物-标相互作用,药物重新定位和药物-药物相互作用.
- 图形卷积网络是核心算法,具有可解释性和数据挑战.
结论:
- *对于用于药物发现的GNN的研究兴趣正在迅速增长.
- *主要挑战包括数据可用性,伦理考虑,计算资源和模型可解释性.
- * 结果提供了对药物发现中GNN的当前趋势和未来研究方向的见解.
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