Lies and Liabilities: Computational Assessment of High-Throughput Screening Hits to Identify Artifact Compounds
Vinícius M. Alves, Adam Yasgar, James Wellnitz, Ganesha Rai, Marielle Rath, Rodolpho C. Braga, Stephen J. Capuzzi, Anton Simeonov, Eugene Muratov, Alexey Zakharov, Alexander Tropsha
Abstract
Hits from high-throughput screening (HTS) of chemical libraries are often false positives due to their interference with assay detection technology. In response, we generated the largest publicly available library of chemical liabilities and developed "Liability Predictor," a free web tool to predict HTS artifacts. More specifically, we generated, curated, and integrated HTS data sets for thiol reactivity, redox activity, and luciferase (firefly and nano) activity and developed and validated quantitative structure-interference relationship (QSIR) models to predict these nuisance behaviors. The resulting models showed 58-78% external balanced accuracy for 256 external compounds per assay. QSIR models developed and validated herein identify nuisance compounds among experimental hits more reliably than do popular PAINS filters. Both the models and the curated data sets were implemented in "Liability Predictor," publicly available at https://liability.mml.unc.edu/. "Liability Predictor" may be used as part of chemical library design or for triaging HTS hits.