GEN: highly efficient SMILES explorer using autodidactic generative examination networks

GEN: highly efficient SMILES explorer using autodidactic generative examination networks

Abstract Recurrent neural networks have been widely used to generate millions of de novo molecules in defined chemical spaces. Reported deep generative models are exclusively based on LSTM and/or GRU units and frequently trained using canonical SMILES. In this study, we introduce Generative Examinat...

Full description

Bibliographic Details
Main Authors: Ruud van Deursen, Peter Ertl, Igor V. Tetko, Guillaume Godin
Format: Article
Language:English
Published: BMC 2020-04-01
Series:Journal of Cheminformatics
Subjects:
Autonomous learning
GEN
GAN
RNN
LSTM
GRU
Online Access:http://link.springer.com/article/10.1186/s13321-020-00425-8

Internet

http://link.springer.com/article/10.1186/s13321-020-00425-8

Similar Items