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Accordingly, ACs reveal structure\u2013activity relationship (SAR) discontinuity and provide SAR information for compound optimization. Herein, we have investigated the question if ACs could be predicted from image data. Therefore, pairs of structural analogs were extracted from different compound activity classes that formed or did not form ACs. From these compound pairs, consistently formatted images were generated. Image sets were used to train and test convolutional neural network (CNN) models to systematically distinguish between ACs and non-ACs. The CNN models were found to predict ACs with overall high accuracy, as assessed using alternative performance measures, hence establishing proof-of-principle. Moreover, gradient weights from convolutional layers were mapped to test compounds and identified characteristic structural features that contributed to successful predictions. Weight-based feature visualization revealed the ability of CNN models to learn chemistry from images at a high level of resolution and aided in the interpretation of model decisions with intrinsic black box character.<\/jats:p>","DOI":"10.1007\/s10822-021-00380-y","type":"journal-article","created":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T11:30:29Z","timestamp":1616153429000},"page":"1157-1164","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Prediction of activity cliffs on the basis of images using convolutional neural networks"],"prefix":"10.1007","volume":"35","author":[{"given":"Javed","family":"Iqbal","sequence":"first","affiliation":[]},{"given":"Martin","family":"Vogt","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0557-5714","authenticated-orcid":false,"given":"J\u00fcrgen","family":"Bajorath","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"380_CR1","unstructured":"Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, Inception-ResNet and the impact of residual connections on learning. arXiv:1602.07261"},{"key":"380_CR2","unstructured":"Goh GB, Siegel C, Vishnu A, Hodas NO, Baker N (2017) Chemception: a deep neural network with minimal chemistry knowledge matches the performance of expert-developed QSAR\/QSPR models. arXiv:1706.06689"},{"key":"380_CR3","doi-asserted-by":"crossref","unstructured":"Goh GB, Vishnu A, Siegel C, Hodas N (2018) Using rule-based labels for weak supervised learning: a ChemNet for transferable chemical property prediction. 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