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. 2019 Jun 19;11:41. doi: 10.1186/s13321-019-0364-5

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© The Author(s) 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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Fig. 1 — KekuleScope framework. a We collected and curated a total of 8 cytotoxicity data sets from ChEMBL version 23. b Compound Kekulé representations were generated for all compounds and used as input to the ConvNets. c We implemented extended versions of 4 commonly used architectures (e.g., VGG-19-bn shown in the figure) by including five additional fully-connected layers to predict pIC₅₀ values on a continuous scale. d The generalization power of the ConvNets was assessed on the test set, and compared to RF models trained using Morgan fingerprints as covariates