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. 2021 Jun 3;12:3307. doi: 10.1038/s41467-021-23165-1

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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 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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Fig. 4 — a Spearman correlation sub-challenge top performer in Round 2 (Q.E.D). b RMSE sub-challenge top performer in Round 2 (AI Winter is Coming). The points correspond to 394 pairs between 25 compounds and 207 kinases. c Ensemble model that combines the top four models selected based on their Spearman correlation in Round 2. d The mean aggregation ensemble model was constructed by adding an increasing number of top-performing models based on their Spearman correlation (the solid curve), until the ensemble correlation dropped below 0.45. The peak performance was reached after aggregating four teams (marked in the legend; see Supplementary Fig. 9 for all the teams. Note: ensemble prediction from a total of 21 best teams had a significantly better Spearman correlation compared to the Q.E.D model alone). The right-hand y-axis and the dotted curve show the Root Mean Square Error (RMSE) of the ensemble model as a function of an increasing number of top-performing models. Source data are provided as a Source Data file⁵⁴.