A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

A self-attention based message passing neural network for predicting molecular lipophilicity and aqueous solubility

Abstract Efficient and accurate prediction of molecular properties, such as lipophilicity and solubility, is highly desirable for rational compound design in chemical and pharmaceutical industries. To this end, we build and apply a graph-neural-network framework called self-attention-based message-p...

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Bibliographic Details
Main Authors: Bowen Tang, Skyler T. Kramer, Meijuan Fang, Yingkun Qiu, Zhen Wu, Dong Xu
Format: Article
Language:English
Published: BMC 2020-02-01
Series:Journal of Cheminformatics
Subjects:
Message passing network
Attention mechanism
Deep learning
Lipophilicity
Aqueous solubility
Online Access:http://link.springer.com/article/10.1186/s13321-020-0414-z

Internet

http://link.springer.com/article/10.1186/s13321-020-0414-z

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