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. 2020 May 20;12:35. doi: 10.1186/s13321-020-00439-2

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© The Author(s) 2020

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 6 — Chemical space coverage by training set S and test sets T_CP and T_MC. T_MC data set consists of 40 HS compounds and 1200 ES compounds, from S and T_CP data sets random samples of 1240 compounds were generated. Each compound was encoded by 1024 bits long ECFP4 fingerprint. The dimensionality of the input space was reduced by SVD to 500 components that explain 88% of the variance in the data