Chemical space exploration guided by deep neural networks. Issue 9 (11th February 2019)

• Record Type: Journal Article
• Authors: Karlov, Dmitry S.; Sosnin, Sergey; Tetko, Igor V.; Fedorov, Maxim V.
• Is Part Of: RSC advances. Volume 9:Issue 9(2019)
• Page Start: 5151
• Page End: 5157
• Publication Date: 2019-02-11
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c8ra10182e
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : A parametric t-SNE approach based on deep feed-forward neural networks was applied to the chemical space visualization problem. Abstract : 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 ).