Hit Dexter: A Machine-Learning Model for the Prediction of Frequent Hitters

ChemMedChem
Conrad StorkJohannes Kirchmair

Abstract

False-positive assay readouts caused by badly behaving compounds-frequent hitters, pan-assay interference compounds (PAINS), aggregators, and others-continue to pose a major challenge to experimental screening. There are only a few in silico methods that allow the prediction of such problematic compounds. We report the development of Hit Dexter, two extremely randomized trees classifiers for the prediction of compounds likely to trigger positive assay readouts either by true promiscuity or by assay interference. The models were trained on a well-prepared dataset extracted from the PubChem Bioassay database, consisting of approximately 311 000 compounds tested for activity on at least 50 proteins. Hit Dexter reached MCC and AUC values of up to 0.67 and 0.96 on an independent test set, respectively. The models are expected to be of high value, in particular to medicinal chemists and biochemists who can use Hit Dexter to identify compounds for which extra caution should be exercised with positive assay readouts. Hit Dexter is available as a free web service at http://hitdexter.zbh. uni-hamburg.de.

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Citations

Aug 17, 2019·Expert Opinion on Drug Discovery·Rafael Ferreira DantasFloriano Paes Silva-Junior
Nov 9, 2019·Future Medicinal Chemistry·Saurabh Loharch, Raman Parkesh
Jun 30, 2018·Future Medicinal Chemistry·Conrad Stork, Johannes Kirchmair
May 23, 2020·International Journal of Molecular Sciences·Neann Mathai, Johannes Kirchmair
Apr 17, 2019·Nature Chemistry·Daniel RekerTiago Rodrigues
Jan 14, 2020·Journal of Medicinal Chemistry·Ziyi YangDongsheng Cao
Aug 8, 2021·Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry·Malte HolmerJohannes Kirchmair
Jan 10, 2019·Journal of Chemical Information and Modeling·Conrad StorkJohannes Kirchmair
Jul 18, 2020·Journal of Chemical Information and Modeling·Vinicius M AlvesAlexander Tropsha

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