An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor

An exploration strategy improves the diversity of de novo ligands using deep reinforcement learning: a case for the adenosine A2A receptor

Abstract Over the last 5 years deep learning has progressed tremendously in both image recognition and natural language processing. Now it is increasingly applied to other data rich fields. In drug discovery, recurrent neural networks (RNNs) have been shown to be an effective method to generate nove...

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Bibliographic Details
Main Authors: Xuhan Liu, Kai Ye, Herman W. T. van Vlijmen, Adriaan P. IJzerman, Gerard J. P. van Westen
Format: Article
Language:English
Published: BMC 2019-05-01
Series:Journal of Cheminformatics
Subjects:
Deep learning
Adenosine receptors
Cheminformatics
Reinforcement learning
Exploration strategy
Online Access:http://link.springer.com/article/10.1186/s13321-019-0355-6

Internet

http://link.springer.com/article/10.1186/s13321-019-0355-6

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