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Most assays in early discovery pipelines today are based on the ability to reliably distinguish between active and inactive compounds, respectively. The subsequent optimization of the active compounds for potency and/or selectivity becomes easier if the inactive compounds are known. However, in many cases, the available inactive leads (or hits) for a target of interest are not good enough to allow the subsequent progression of the compounds toward target-based lead optimization. Here, we describe a strategy for the rapid identification and prioritization of potential hits by combining structure- and activity-based modelling. Applying both approaches to a highly diverse set of kinase activities we
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