Chemical space exploration guided by deep neural networks

Dmitry S. Karlov, Sergey Sosnin, Igor V. Tetko, Maxim V. Fedorov

    Research output: Contribution to journalArticlepeer-review

    17 Citations (Scopus)

    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).

    Original languageEnglish
    Pages (from-to)5151-5157
    Number of pages7
    JournalRSC Advances
    Volume9
    Issue number9
    DOIs
    Publication statusPublished - 2019

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