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Chemical space exploration guided by deep neural networks

Author:
Karlov, Dmitry S., Sosnin, Sergey, Tetko, Igor V., Fedorov, Maxim V.
Source:
RSC advances 2019 v.9 no.9 pp. 5151-5157
ISSN:
2046-2069
Subject:
multidimensional scaling, neural networks, principal component analysis, world wide web
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).
Agid:
6312631