University of Vienna, Department of Pharmaceutical Chemistry, Althanstraße 14, A-1090 Vienna, Austria.
Servier Research Institute - CentEx Biotechnology, 125 Chemin de Ronde, 78290 Croissy-sur-Seine, France.
Drug Discov Today Technol. 2020 Dec;37:1-12. doi: 10.1016/j.ddtec.2020.11.009. Epub 2020 Dec 17.
As graph neural networks are becoming more and more powerful and useful in the field of drug discovery, many pharmaceutical companies are getting interested in utilizing these methods for their own in-house frameworks. This is especially compelling for tasks such as the prediction of molecular properties which is often one of the most crucial tasks in computer-aided drug discovery workflows. The immense hype surrounding these kinds of algorithms has led to the development of many different types of promising architectures and in this review we try to structure this highly dynamic field of AI-research by collecting and classifying 80 GNNs that have been used to predict more than 20 molecular properties using 48 different datasets.
随着图神经网络在药物发现领域变得越来越强大和有用，许多制药公司对将这些方法用于自己的内部框架产生了兴趣。对于预测分子性质等任务来说，这尤其具有吸引力，因为预测分子性质通常是计算机辅助药物发现工作流程中最关键的任务之一。围绕这些算法的巨大炒作导致了许多不同类型的有前途的架构的发展，在这篇综述中，我们通过收集和分类 80 个 GNN 来尝试构建这个高度动态的人工智能研究领域，这些 GNN 已经被用于使用 48 个不同的数据集预测超过 20 种分子性质。
