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. 2021 Jan 12;11:525. doi: 10.1038/s41598-020-80113-7

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© The Author(s) 2021

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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Effect of the scaffold diversity of the datasets on the prediction performance. (a) The probability distribution of the scaffolds in ChEMBL. Compared to the uniform distribution, ChEMBL is much more biased towards smaller scaffolds, resulting in a smaller H value. (b) The probability distribution of the scaffolds in the dataset for the serotonin transporter (SERT) and serotonin 1A receptor (5-HT1A). The H value is larger for 5-HT1A, whose scaffold distribution is wider than that of SERT. (c) Violin plots of the H value distribution by protein family. The number after each name on the x-axis shows the number of targets in each family. (d–f) Effect of the scaffold diversity (d), the dissimilarity of the scaffold distribution (e), and the training set size (f) on the MAE.