Molecular Pharmaceutics, volume 14, issue 9, pages 3098-3104

druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico

Artur Kadurin 1, 2, 3
Sergey I. Nikolenko 2, 3, 4
Kuzma Khrabrov 5
Alexander Aliper 1
Publication typeJournal Article
Publication date2017-08-04
Quartile SCImago
Q1
Quartile WOS
Q1
Impact factor4.5
ISSN15438384, 15438392
Drug Discovery
Pharmaceutical Science
Molecular Medicine
Abstract
Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.

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GOST |
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GOST Copy
Kadurin A. et al. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico // Molecular Pharmaceutics. 2017. Vol. 14. No. 9. pp. 3098-3104.
GOST all authors (up to 50) Copy
Kadurin A., Nikolenko S. I., Khrabrov K., Aliper A., Zhavoronkov A. druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico // Molecular Pharmaceutics. 2017. Vol. 14. No. 9. pp. 3098-3104.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1021/acs.molpharmaceut.7b00346
UR - https://doi.org/10.1021/acs.molpharmaceut.7b00346
TI - druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
T2 - Molecular Pharmaceutics
AU - Khrabrov, Kuzma
AU - Zhavoronkov, Alex
AU - Kadurin, Artur
AU - Nikolenko, Sergey I.
AU - Aliper, Alexander
PY - 2017
DA - 2017/08/04
PB - American Chemical Society (ACS)
SP - 3098-3104
IS - 9
VL - 14
SN - 1543-8384
SN - 1543-8392
ER -
BibTex |
Cite this
BibTex Copy
@article{2017_Kadurin,
author = {Kuzma Khrabrov and Alex Zhavoronkov and Artur Kadurin and Sergey I. Nikolenko and Alexander Aliper},
title = {druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico},
journal = {Molecular Pharmaceutics},
year = {2017},
volume = {14},
publisher = {American Chemical Society (ACS)},
month = {aug},
url = {https://doi.org/10.1021/acs.molpharmaceut.7b00346},
number = {9},
pages = {3098--3104},
doi = {10.1021/acs.molpharmaceut.7b00346}
}
MLA
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MLA Copy
Kadurin, Artur, et al. “druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico.” Molecular Pharmaceutics, vol. 14, no. 9, Aug. 2017, pp. 3098-3104. https://doi.org/10.1021/acs.molpharmaceut.7b00346.
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