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Chemical space exploration guided by deep neural networks.
RSC Adv. 9, 5151-5157 (2019)
A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem. It is able to retain more information than certain dimensionality reduction techniques used for this purpose (principal component analysis (PCA), multidimensional scaling (MDS)). The applicability of this method to some chemical space navigation tasks (activity cliffs and activity landscapes identification) is discussed. We created a simple web tool to illustrate our work (http://space.syntelly.com).
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Times Cited
Scopus
Cited By
Cited By
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3.049
0.785
15
17
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Visualization; Prediction; Regression; Chemistry; Database; Universe; Qsar
Language
english
Publication Year
2019
HGF-reported in Year
2019
ISSN (print) / ISBN
2046-2069
e-ISSN
2046-2069
Journal
RSC Advances
Quellenangaben
Volume: 9,
Issue: 9,
Pages: 5151-5157
Publisher
Royal Society of Chemistry (RSC)
Publishing Place
London
Reviewing status
Peer reviewed
Institute(s)
Institute of Structural Biology (STB)
POF-Topic(s)
30203 - Molecular Targets and Therapies
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-503000-001
WOS ID
WOS:000459183800055
Scopus ID
85061977421
Erfassungsdatum
2019-03-19