|Maksim Koptelov||University of Caen Normandy|
|Albrecht Zimmermann||University of Caen Normandy|
|Pascal Bonnet||University of Orléans|
|Ronan Bureau||University of Caen Normandy|
|Bruno Crémilleux||University of Caen Normandy|
This paper studies Pan Assays Interference Compounds (PAINS). The authors are developing a tool, PrePeP, that predicts PAINS, and allows experts to visually explore the reasons for the prediction.
Pan Assays Interference Compounds (PAINS) are a significant problem in modern drug discovery: compounds showing non-target specific activity in high-throughput screening can mislead medicinal chemists during hit identification, wasting time and resources. Recent work has shown that existing structural alerts are not up to the task of identifying PAINS. To address this short-coming, we are in the process of developing a tool, PrePeP, that predicts PAINS, and allows experts to visually explore the reasons for the prediction. In the paper, we discuss the different aspects that are involved in developing a functional tool: systematically deriving structural descriptors, addressing the extreme imbalance of the data, offering visual information that pharmacological chemists are familiar with. We evaluate the quality of the approach using benchmark data sets from the literature and show that we correct several short-comings of existing PAINS alerts that have recently been pointed out.