Molecular substructure graph attention network for molecular property identification in drug discovery
作者：
Highlights：
• We propose to use a structural feature extraction scheme including 3 types of features (raw + tree decomposition + ECFP).
• We design a framework including several graph attention convolutional (GAC) blocks and deep neural network (DNN) blocks to process the above structural features.
• We design a readout block based on gated recurrent units (GRU). The readout blocks collaborate with the GAC blocks to obtain molecular embeddings.
• We visualize molecules and mark the important atoms with attention scores of MSSGAT, which can be a good reference for subsequent drug development.
摘要
•We propose to use a structural feature extraction scheme including 3 types of features (raw + tree decomposition + ECFP).•We design a framework including several graph attention convolutional (GAC) blocks and deep neural network (DNN) blocks to process the above structural features.•We design a readout block based on gated recurrent units (GRU). The readout blocks collaborate with the GAC blocks to obtain molecular embeddings.•We visualize molecules and mark the important atoms with attention scores of MSSGAT, which can be a good reference for subsequent drug development.
论文关键词：Molecular substructure,Graph attention,Molecular property identification
论文评审过程：Received 7 July 2021, Revised 27 January 2022, Accepted 16 March 2022, Available online 18 March 2022, Version of Record 26 March 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108659
