Randomized SMILES strings improve the quality of molecular generative models

Randomized SMILES strings improve the quality of molecular generative models

Abstract Recurrent Neural Networks (RNNs) trained with a set of molecules represented as unique (canonical) SMILES strings, have shown the capacity to create large chemical spaces of valid and meaningful structures. Herein we perform an extensive benchmark on models trained with subsets of GDB-13 of...

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Bibliographic Details
Main Authors: Josep Arús-Pous, Simon Viet Johansson, Oleksii Prykhodko, Esben Jannik Bjerrum, Christian Tyrchan, Jean-Louis Reymond, Hongming Chen, Ola Engkvist
Format: Article
Language:English
Published: BMC 2019-11-01
Series:Journal of Cheminformatics
Subjects:
Deep learning
Generative models
SMILES
Randomized SMILES
Recurrent Neural Networks
Chemical databases
Online Access:http://link.springer.com/article/10.1186/s13321-019-0393-0

Internet

http://link.springer.com/article/10.1186/s13321-019-0393-0

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