Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling-Reference-Cited by-同舟云学术

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Parallel Generative Topographic Mapping: An Efficient Approach for Big Data Handling

Published:2020-04-29 Issue:12 Volume:39 Page:2000009
ISSN:1868-1743
Container-title:Molecular Informatics
language:en
Short-container-title:Mol. Inf.

Author:

Lin Arkadii¹,Baskin Igor I.²,Marcou Gilles¹,Horvath Dragos¹,Beck Bernd³,Varnek Alexandre¹^ORCID

Affiliation:

1. University of Strasbourg Laboratory of Chemoinformatics, Faculty of Chemistry 4, Blaise Pascal str. 67081 Strasbourg France

2. Faculty of Physics Lomonosov Moscow State University 1/2, Leninskie Gory str. 119991 Moscow Russia

3. Department of Medicinal Chemistry Boehringer Ingelheim Pharma GmbH & Co. KG 65, Birkendorfer str. 88397 Biberach an der Riss Germany

Funder

European Union's Horizon 2020 research and innovation program

Marie Skłodowska-Curie Grant

Publisher

Wiley

Subject

Organic Chemistry,Computer Science Applications,Drug Discovery,Molecular Medicine,Structural Biology

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/minf.202000009

Reference40 articles.

1. The ChEMBL database in 2017

2. PubChem Substance and Compound databases

3. “Chemical Abstract Service ” can be found under https://www.cas.org/about/cas-content2019.

4. Enumeration of 166 Billion Organic Small Molecules in the Chemical Universe Database GDB-17

5. Chemography: The Art of Navigating in Chemical Space

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