Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficie...

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Bibliographic Details
Published in:	Journal of cheminformatics Vol. 11; no. 1; pp. 70 - 10
Main Authors:	Kwon, Youngchun, Yoo, Jiho, Choi, Youn-Suk, Son, Won-Joon, Lee, Dongseon, Kang, Seokho
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 21.11.2019 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Artificial neural networks Chemistry Chemistry and Materials Science Computational Biology/Bioinformatics Computer Applications in Chemistry Deep learning Documentation and Information in Chemistry Graph matching Graph neural network Graph neural networks Graphs Machine learning Molecular graph Neural networks Organic chemistry Research Article Theoretical and Computational Chemistry Variational autoencoder Deep learning Variational autoencoder Molecular graph Graph neural network
ISSN:	1758-2946, 1758-2946
Online Access:	Get full text
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