High-throughput screening is a key technology in early drug design that enables the screening of tens of thousands of compounds per day. [1,2] However, false-positive signals triggered by badly behaving compounds (frequent hitters, pan-assay interference compounds, aggregators and others) continue to pose a major challenge in early drug discovery and still lead to a substantial number of false hits reported in the scientific literature. [3] Few computational approaches that allow the identification of badly behaving compounds exist, and the applicability of these existing methods is limited. We present Hit Dexter, a web service that allows the identification of frequent hitters with high accuracy. [4] Hit Dexter is based on two extremely randomized tree classifiers trained on a well-prepared subset of the PubChem Bioassay database containing 311k compounds tested on at least 50 proteins each. We show that Hit Dexter is able to discriminate non-promiscuous from promiscuous and highly-promiscuous compounds of large external test sets with MCC and AUC values of up to 0.67 and 0.96, respectively. Hit Dexter is available as a free web service at
The models were taken from the PubChem BioAssay database consisting of 311k compounds REF .