Graph Neural Networks in Predicting Protein Function and Interactions
Graph Neural Networks in Predicting Protein Function and Interactions
复制标题
图神经网络预测蛋白质功能和相互作用
DOI：
10.1007/978-981-16-6054-2_25
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Shehu, Amarda
中科院分区：
文献类型：
--
作者：
Kabir, Anowarul;Shehu, Amarda
图神经网络 (GNN) 由于能够对非欧几里得数据（例如图）进行操作，因此在分子建模研究中正变得越来越流行和强大的工具。由于 GNN 能够在图中嵌入固有结构并保留语义信息，因此它正在推进各种分子结构功能研究。在本章中，我们重点关注 GNNaided 研究，这些研究汇集了一个或多个以蛋白质为中心的数据源，目的是阐明蛋白质功能。我们对 GNN 及其最成功的最新变体进行了简短的调查，旨在解决预测蛋白质分子的生物功能和分子相互作用的相关问题。我们回顾了最新的方法论进展、发现以及有望促进进一步研究的开放挑战。
Graph Neural Networks (GNNs) are becoming increasingly popular and powerful tools in molecular modeling research due to their ability to operate over non-Euclidean data, such as graphs. Because of their ability to embed both the inherent structure and preserve the semantic information in a graph, GNNs are advancing diverse molecular structure-function studies. In this chapter, we focus on GNNaided studies that bring together one or more protein-centric sources of data with the goal of elucidating protein function. We provide a short survey on GNNs and their most successful, recent variants designed to tackle the related problems of predicting the biological function and molecular interactions of protein molecules. We review the latest methodological advances, discoveries, as well as open challenges promising to spur further research.