Chemical space exploration guided by deep neural networks
Open Access
- 10 February 2019
- journal article
- research article
- Published by Royal Society of Chemistry (RSC) in RSC Advances
- Vol. 9 (9), 5151-5157
- https://doi.org/10.1039/c8ra10182e
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).Funding Information
- Bundesministerium für Bildung und Forschung (01KL1710)
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