A de novo molecular generation method using latent vector based generative adversarial network
Artikel i vetenskaplig tidskrift, 2019

Deep learning methods applied to drug discovery have been used to generate novel structures. In this study, we propose a new deep learning architecture, LatentGAN, which combines an autoencoder and a generative adversarial neural network for de novo molecular design. We applied the method in two scenarios: One to generate random drug-like compounds and another to generate target-biased compounds. Our results show that the method works well in both cases. Sampled compounds from the trained model can largely occupy the same chemical space as the training set and also generate a substantial fraction of novel compounds. Moreover, the drug-likeness score of compounds sampled from LatentGAN is also similar to that of the training set. Lastly, generated compounds differ from those obtained with a Recurrent Neural Network-based generative model approach, indicating that both methods can be used complementarily.[Figure not available: See fulltext.]

Molecular design

Deep learning

Autoencoder networks

Generative adversarial networks


Oleksii Shevtsov

Student vid Chalmers

AstraZeneca AB

Simon Johansson

Chalmers, Data- och informationsteknik, Data Science

AstraZeneca AB

Panagiotis Christos Kotsias

AstraZeneca AB

Josep Arús-Pous

Universität Bern

AstraZeneca AB

Esben Jannik Bjerrum

AstraZeneca AB

Ola Engkvist

AstraZeneca AB

Hongming Chen

Chinese Academy of Sciences

AstraZeneca AB

Journal of Cheminformatics

1758-2946 (ISSN)

Vol. 11 1 74


Farmaceutisk vetenskap

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik



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