Cited by 279Computational Drug Discovery MethodsMachine Learning in Materials ScienceProtein Degradation and Inhibitors

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Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

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Polykovskiy, Daniil, Zhebrak, Alexander, Vetrov, Dmitry, Ivanenkov, Yan, Aladinskiy, Vladimir, Mamoshina, Polina, Bozdaganyan, Marine, Aliper, Alexander, Zhavoronkov, Alex, Kadurin, Artur·2018·Molecular Pharmaceutics

DOI 10.1021/acs.molpharmaceut.8b00839

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Abstract

Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular structures within hours, while conventional drug discovery pipelines require months of work. In this article, we propose a new generative architecture, entangled conditional adversarial autoencoder, that generates molecular structures based on various properties, such as activity against a specific protein, solubility, or ease of synthesis. We apply the proposed model to generate a novel inhibitor of Janus kinase 3, implicated in rheumatoid arthritis, psoriasis, and vitiligo. The discovered molecule was tested in vitro and showed good activity and selectivity.

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Self-citation

Authors: Polykovskiy, D., Zhebrak, A., Vetrov, D., Ivanenkov, Y., Aladinskiy, V., Mamoshina, P., Bozdaganyan, M., Aliper, A., Zhavoronkov, A., & Kadurin, A.
Affiliations: National Research University Higher School of Economics (RU)
Insilico Medicine (United States) (US)
Russian Academy of Sciences (RU)
Moscow Institute of Physics and Technology (RU)
Corresponding author: Polykovskiy, Daniil
ORCID: Polykovskiy, Daniil
Zhebrak, Alexander
Vetrov, Dmitry
Ivanenkov, Yan
Aladinskiy, Vladimir
Mamoshina, Polina
Bozdaganyan, Marine
Aliper, Alexander
Zhavoronkov, Alex
Kadurin, Artur
Venue: Molecular Pharmaceutics
Publisher: American Chemical Society (ACS)
Published: 2018-09-04
Volume: 15
Issue: 10
Pages: 4398-4405
ISSN: 1543-8384 (ISSN-L)
1543-8392
DOI: 10.1021/acs.molpharmaceut.8b00839
OpenAlex: W2891868449
Cited by: 278
References: 52
Citations by year: 2026: 20 · 2025: 32 · 2024: 46 · 2023: 39 · 2022: 24 · 2021: 43 · 2020: 42 · 2019: 27 · 2018: 4
FWCI: 21.88
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
pubs.acs.org/page/policy/authorchoice_termsofuse.html (vor, 15 d)
Locations: Molecular Pharmaceutics (publishedVersion, other-oa)
PubMed (publishedVersion)
Funders: Ministry of Education and Science of the Russian Federation (20.9907.2017/VU)
Russian Science Foundation (17-71-20072, 17-74-30012)
Topics: Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Protein Degradation and Inhibitors
Keywords: generative models, de novo drug discovery, molecular generation, rheumatoid arthritis, psoriasis, vitiligo, solubility, synthetic accessibility, drug-likeness
MeSH terms: Machine Learning, Animals, Humans, Neural Networks, Computer, Janus Kinase 3, Drug Discovery

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Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery

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