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

Molecular Pharmaceutics

Volumn 14, Issue 9, 2017, Pages 3098-3104

  Kadurin, Artur ^a,c,g   Nikolenko, Sergey ^b,c,g   Khrabrov, Kuzma ^d   Aliper, Alex ^a   Zhavoronkov, Alex ^a,e,f  

^a JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b NATIONAL RESEARCH UNIVERSITY HIGHER SCHOOL OF ECONOMICS   (Russian Federation)

^c STEKLOV MATHEMATICAL INSTITUTE   (Russian Federation)

^d Building 2   (Russian Federation)

^e The Biogerontology Research Foundation   (United Kingdom)

^f MOSCOW INSTITUTE OF PHYSICS AND TECHNOLOGY   (Russian Federation)

^g KAZAN FEDERAL UNIVERSITY   (Russian Federation)

Author keywords

adversarial autoencoder; deep learning; drug discovery; generative adversarial network; variational autoencoder

Indexed keywords

ADVERSARIAL AUTOENCODER MODEL; ARTICLE; CHEMICAL STRUCTURE; COMPUTER MODEL; EVALUATION STUDY; MOLECULAR MODEL; MOLECULE; PRIORITY JOURNAL; SAMPLING; ARTIFICIAL INTELLIGENCE; ARTIFICIAL NEURAL NETWORK; COMPUTER SIMULATION; CONCEPT FORMATION; LEARNING; THEORETICAL MODEL;

ARTIFICIAL INTELLIGENCE; COMPUTER SIMULATION; CONCEPT FORMATION; LEARNING; MODELS, THEORETICAL; NEURAL NETWORKS (COMPUTER);

EID: 85028890248     PISSN: 15438384     EISSN: 15438392     Source Type: Journal    
DOI: 10.1021/acs.molpharmaceut.7b00346     Document Type: Article

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