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
1
GoodfellowI.Pouget-AbadieJ.MirzaM.XuB.Warde-FarleyD.OzairS.et al. (2014). “Generative adversarial nets,” in Advances in Neural Information Processing Systems. (Curran Associates, Inc), vol. 27, 2672–2680.
2
Méndez-LucioO.BaillifB.ClevertD.-A.RouquiéD.WichardJ. (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
Summary
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
Volume
11 - 2020
Edited and reviewed by
Alastair George Stewart, The University of Melbourne, Australia
Updates
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
This article was submitted to Translational Pharmacology, a section of the journal Frontiers in Pharmacology
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