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Karlov, D.S.* ; Sosnin, S.* ; Tetko, I.V. ; Fedorov, M.V.*

Chemical space exploration guided by deep neural networks.

RSC Adv. 9, 5151-5157 (2019)
Verlagsversion Forschungsdaten DOI
Open Access Gold
Creative Commons Lizenzvertrag
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).
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Visualization; Prediction; Regression; Chemistry; Database; Universe; Qsar
ISSN (print) / ISBN 2046-2069
e-ISSN 2046-2069
Zeitschrift RSC Advances
Quellenangaben Band: 9, Heft: 9, Seiten: 5151-5157 Artikelnummer: , Supplement: ,
Verlag Royal Society of Chemistry (RSC)
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Structural Biology (STB)