De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping-Reference-Cited by-同舟云学术

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De Novo Molecular Design by Combining Deep Autoencoder Recurrent Neural Networks with Generative Topographic Mapping

Published:2019-02-20 Issue:3 Volume:59 Page:1182-1196
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Sattarov Boris¹,Baskin Igor I.²^ORCID,Horvath Dragos¹^ORCID,Marcou Gilles¹,Bjerrum Esben Jannik³^ORCID,Varnek Alexandre¹^ORCID

Affiliation:

1. Laboratory of Chemoinformatics, UMR 7177 University of Strasbourg/CNRS, 4 rue B. Pascal, 67000 Strasbourg, France

2. Faculty of Physics, M.V. Lomonosov Moscow State University, Leninskie Gory, Moscow 19991, Russia

3. Wildcard Pharmaceutical Consulting, Zeaborg Science Center, Frødings Allé 41, 2860 Søborg, Denmark

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.8b00751

Reference90 articles.

1. Chemoinformatics as a Theoretical Chemistry Discipline

2. Virtual screening—an overview

3. Quo Vadis, Virtual Screening? A Comprehensive Survey of Prospective Applications

4. State-of-the-art in ligand-based virtual screening

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