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. 2022 Mar 31;9:139. doi: 10.1038/s41597-022-01243-3

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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 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. 1 — Methodology of data collection and categorization. (a) Presentation of workflow for data collection. Gathered data was examined and categorized allowing to establish final list of 141 inhibitory compounds. (b) Outline of tables used for creation of SINBAD. Gathered data was put into 6 tables which are linked with each other (marked with red) by one variable - specific number each compound was given in initially created table COMPOUNDS. (c) Graphical data available in SINBAD. We provide structures of compounds and for some we visualized ligand – protein interaction using STAT-SH2 3D models and Surflex -Dock 2.6.