The DOI matches its Crossref record; 51 of the 52 references it cites were found in a registry or an index.
Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery
Open accessAbstract
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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“Hopfinger (100) framed this discussion in terms of computer-aided drug design in 1985.”
From Artificial Intelligence for Drug Discovery: Are We There Yet? (2024) · cites this paper as [100]
Citations
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Cited by
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Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573
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Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
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Self-citationCombining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Metadata
- 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
- 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
- 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
- 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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