论文标题
在药物发现和开发中利用图机学习
Utilising Graph Machine Learning within Drug Discovery and Development
论文作者
论文摘要
Graph Machine Learning（GML）在药品和生物技术行业中正在获得日益增长的兴趣，因为它可以对生物分子结构，它们之间的功能关系以及整合多OMIC数据集进行建模的能力以及其他数据类型。在此，我们在药物发现和开发的背景下对该主题进行了多学科学术工业审查。在介绍了关键术语和建模方法之后，我们按时间顺序通过药物开发管道移动，以识别和总结合并的工作：目标识别，小分子和生物制剂的设计以及药物重新利用。尽管该领域仍在出现，但关键的里程碑，包括重新利用的药物进入体内研究，这表明Graph Machine Learning将成为生物医学机器学习中选择的建模框架。
Graph Machine Learning (GML) is receiving growing interest within the pharmaceutical and biotechnology industries for its ability to model biomolecular structures, the functional relationships between them, and integrate multi-omic datasets - amongst other data types. Herein, we present a multidisciplinary academic-industrial review of the topic within the context of drug discovery and development. After introducing key terms and modelling approaches, we move chronologically through the drug development pipeline to identify and summarise work incorporating: target identification, design of small molecules and biologics, and drug repurposing. Whilst the field is still emerging, key milestones including repurposed drugs entering in vivo studies, suggest graph machine learning will become a modelling framework of choice within biomedical machine learning.
