Graph Neural Networks for Molecules
It provides a comprehensive overview for researchers in computational chemistry and drug discovery, but it is incremental as it summarizes existing work without presenting new results.
This review tackles the problem of modeling molecular systems by introducing graph neural networks (GNNs) and their applications, such as property prediction and molecular generation, highlighting their suitability for learning from graphical data.
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.