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Issue 9, 2019, Issue in Progress
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Chemical space exploration guided by deep neural networks

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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).

Graphical abstract: Chemical space exploration guided by deep neural networks

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Publication details

The article was received on 11 Dec 2018, accepted on 29 Jan 2019 and first published on 11 Feb 2019


Article type: Paper
DOI: 10.1039/C8RA10182E
RSC Adv., 2019,9, 5151-5157
  • Open access: Creative Commons BY-NC license
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    Chemical space exploration guided by deep neural networks

    D. S. Karlov, S. Sosnin, I. V. Tetko and M. V. Fedorov, RSC Adv., 2019, 9, 5151
    DOI: 10.1039/C8RA10182E

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