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Front. Pharmacol., 21 August 2020 | https://doi.org/10.3389/fphar.2020.01236

Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

  • 1Insilico Medicine, Hong Kong, Hong Kong
  • 2Neuromation OU, Tallinn, Estonia

An Addendum on
Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders

By Shayakhmetov R, Kuznetsov M, Zhebrak A, Kadurin A, Nikolenko S, Aliper A and Polykovskiy D (2020). Front. Pharmacol. 11:269. doi: 10.3389/fphar.2020.00269

In the original article, we missed the parallel work by Méndez-Lucio et al. (2020). This work also tackles a similar problem of generating molecular structures from transcriptomic data. The authors proposed a conditional model based on the generative adversarial networks Goodfellow et al. (2014). Unlike their approach, our model is joint, allowing us to generate molecular structures for a given gene expression profile and vice versa.

References

Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., et al. (2014). “Generative adversarial nets,” in Advances in Neural Information Processing Systems. (Curran Associates, Inc), vol. 27, 2672–2680.

Google Scholar

Méndez-Lucio, O., Baillif, B., Clevert, D.-A., Rouquié, D., Wichard, J. (2020). De novo generation of hit-like molecules from gene expression signatures using artificial intelligence. Nat. Commun. 11, 1–10. doi: 10.1038/s41467-019-13807-w

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Keywords: deep learning, generative models, adversarial autoencoders, conditional generation, representation learning, drug discovery, gene expression

Citation: Shayakhmetov R, Kuznetsov M, Zhebrak A, Kadurin A, Nikolenko S, Aliper A and Polykovskiy D (2020) Addendum: Molecular Generation for Desired Transcriptome Changes With Adversarial Autoencoders. Front. Pharmacol. 11:1236. doi: 10.3389/fphar.2020.01236

Received: 26 June 2020; Accepted: 28 July 2020;
Published: 21 August 2020.

Edited and reviewed by: Alastair George Stewart, The University of Melbourne, Australia

Copyright © 2020 Shayakhmetov, Kuznetsov, Zhebrak, Kadurin, Nikolenko, Aliper and Polykovskiy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Daniil Polykovskiy, daniil@insilico.com

These authors have contributed equally to this work