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. 2022 Jan 10;12:410. doi: 10.1038/s41598-021-04448-5

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© The Author(s) 2022

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 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/.

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(a) OOB MSE for each of the leave-one-out models corresponding to forests created from docking features of 3 and 10 most important receptors with or without 8 molecular features. The most difficult ligands to predict are labeled. (b) The enrichment rate of RF models compared to the most and the least accurate docking strategies. (c) Distribution of prediction errors of the most accurate RF model in comparison with the most accurate docking without learning strategy. (d) Distribution of prediction errors compared for some representative models and their MSE and Pearson’s correlation coefficient between predicted and experimental values.