druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico-Reference-Cited by-同舟云学术

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

Published:2017-08-04 Issue:9 Volume:14 Page:3098-3104
ISSN:1543-8384
Container-title:Molecular Pharmaceutics
language:en
Short-container-title:Mol. Pharmaceutics

Author:

Kadurin Artur¹²³,Nikolenko Sergey⁴²³,Khrabrov Kuzma⁵,Aliper Alex¹,Zhavoronkov Alex¹⁶⁷^ORCID

Affiliation:

1. Pharmaceutical Artificial Intelligence Department, Insilico Medicine, Inc., Emerging Technology Centers, Johns Hopkins University at Eastern, Baltimore, Maryland 21218, United States

2. Steklov Mathematical Institute at St. Petersburg, St. Petersburg 191023, Russia

3. Kazan Federal University, Kazan, Republic of Tatarstan 420008, Russia

4. National Research University Higher School of Economics, St. Petersburg 190008, Russia

5. Search Department, Mail.Ru Group Ltd., Moscow 125167, Russia

6. The Biogerontology Research Foundation, Trevissome Park, Truro TR4 8UN, U.K.

7. Moscow Institute of Physics and Technology, Dolgoprudny 141701, Russia

Funder

Government of the Russian Federation

Publisher

American Chemical Society (ACS)

Subject

Drug Discovery,Pharmaceutical Science,Molecular Medicine

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.molpharmaceut.7b00346

Reference31 articles.

1. How to Revive Breakthrough Innovation in the Pharmaceutical Industry

2. Why and how have drug discovery strategies in pharma changed? What are the new mindsets?

3. Druggable chemical space and enumerative combinatorics

4. Design of efficient computational workflows for in silico drug repurposing

5. Deep learning

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