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. 2019 Mar 11;9:4055. doi: 10.1038/s41598-019-40640-4

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© The Author(s) 2019

Open Access This 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

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(A) Scatter plot of predictions (Bayesian scores) of model A (all actives vs inactives) and model B (most potent actives vs inactives) for 1280 compounds from the Sigma Aldrich Flavor and Fragrance catalog. The highlighted 138 highest probability actives (red) were selected by EstPGood predictions >0.9 for both models. The highlighted 82 highest probability inactives (blue) were selected by EstPGood <0.1 for both models. (B) Histogram of predictions (Bayesian scores) of model A, showing all predicted actives (red) and predicted inactives (blue) from panel A. (C) Histogram of predictions (Bayesian scores) of model B, showing all predicted actives (red) and predicted inactives (blue) from panel A.