Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models

Abstract Graph neural networks (GNN) has been considered as an attractive modelling method for molecular property prediction, and numerous studies have shown that GNN could yield more promising results than traditional descriptor-based methods. In this study, based on 11 public datasets covering var...

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Bibliographic Details
Main Authors: Dejun Jiang, Zhenxing Wu, Chang-Yu Hsieh, Guangyong Chen, Ben Liao, Zhe Wang, Chao Shen, Dongsheng Cao, Jian Wu, Tingjun Hou
Format: Article
Language:English
Published: BMC 2021-02-01
Series:Journal of Cheminformatics
Subjects:
Graph neural networks
Extreme gradient boosting
Ensemble learning
Deep learning
ADME/T prediction
Online Access:https://doi.org/10.1186/s13321-020-00479-8

Internet

https://doi.org/10.1186/s13321-020-00479-8

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