Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Integrating concept of pharmacophore with graph neural networks for chemical property prediction and interpretation

Abstract Recently, graph neural networks (GNNs) have revolutionized the field of chemical property prediction and achieved state-of-the-art results on benchmark data sets. Compared with the traditional descriptor- and fingerprint-based QSAR models, GNNs can learn task related representations, which...

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Bibliographic Details
Main Authors: Yue Kong, Xiaoman Zhao, Ruizi Liu, Zhenwu Yang, Hongyan Yin, Bowen Zhao, Jinling Wang, Bingjie Qin, Aixia Yan
Format: Article
Language:English
Published: BMC 2022-08-01
Series:Journal of Cheminformatics
Subjects:
Graph neural networks (GNNs)
Pharmacophore
Reduced graph (RG)
Hierarchical pooling
Online Access:https://doi.org/10.1186/s13321-022-00634-3

Internet

https://doi.org/10.1186/s13321-022-00634-3

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