Hit Dexter: A Machine‐Learning Model for the Prediction of Frequent Hitters. (1st February 2018) - Stork, Conrad; Wagner, Johannes; Friedrich, Nils‐Ole; de Bruyn Kops, Christina; Šícho, Martin; Kirchmair, Johannes
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 athttp://hitdexter.zbh. uni‐hamburg.de . Abstract : Hit Dexter : False‐positive assay signals triggered by badly behaving compounds continue to pose a major challenge to experimental screening. A free web service, called Hit Dexter, is able to identify such compounds with high accuracy, enabling chemists to make better‐informed decisions on their hit compounds.