论文标题
分子图神经网络
Graph Neural Networks for Molecules
论文作者
论文摘要
能够从图形数据中学习表示形式的图形神经网络（GNN）自然适合对分子系统进行建模。这篇综述介绍了GNN及其对小有机分子的各种应用。 GNNS依靠消息通用操作（一种通用而强大的框架）来迭代更新节点功能。许多研究设计了GNN体系结构，以有效地学习2D分子图的拓扑信息以及3D分子系统的几何信息。 GNN已在各种分子应用中实施，包括分子属性预测，分子评分和对接，分子优化和从头产生，分子动力学模拟等。此外，综述还总结了近期与GNNS分子的自学学习的最新发展。
Graph neural networks (GNNs), which are capable of learning representations from graphical data, are naturally suitable for modeling molecular systems. This review introduces GNNs and their various applications for small organic molecules. GNNs rely on message-passing operations, a generic yet powerful framework, to update node features iteratively. Many researches design GNN architectures to effectively learn topological information of 2D molecule graphs as well as geometric information of 3D molecular systems. GNNs have been implemented in a wide variety of molecular applications, including molecular property prediction, molecular scoring and docking, molecular optimization and de novo generation, molecular dynamics simulation, etc. Besides, the review also summarizes the recent development of self-supervised learning for molecules with GNNs.
