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. 2021 Jun 14;4:90. doi: 10.1038/s42004-021-00528-9

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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. 1 — a The log P dataset was randomly split in training set, validation set and test set for DNN model development. The DNN is depicted schematically. b The dataset itself includes heterogeneous chemical structures, which can be characterized by the number of non-hydrogen atoms NHA, the number of functional groups, substance classes like aliphatic and aromatic chemicals as well as heteroatoms (O—oxygen, N—nitrogen, S—sulfur, P—phosphorous, hal.—halogens fluorine, chlorine, bromine, and iodine) included in the chemical’s structure. Further we distinguish between potential ions or neutral chemicals. c DNN prediction for the randomly selected test set (DNN_taut applied). In gray neutral chemicals are depicted, in red potential ions (anions, cations, and zwitterions) are marked.