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Entangled Conditional Adversarial Autoencoder for de-Novo Drug Discoveryстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Polykovskiy Daniil, Zhebrak Alexander, Vetrov Dmitry, ivanenkov yan, Aladinskiy Vladimir, Bozdaganyan Marine, Mamoshina Polina, Aliper Alex, Zhavoronkov Alex, Kadurin Artur
- Журнал: Molecular Pharmaceutics
- Год издания: 2018
- Издательство: American Chemical Society
- Местоположение издательства: United States
- DOI: 10.1021/acs.molpharmaceut.8b00839
- Аннотация: 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 auto-encoder, 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.
- Добавил в систему: Боздаганян Маринэ Евгеньевна