Quantification of frequent-hitter behavior based on historical high-throughput screening data

We mine historical high-throughput data to identify and characterize 'frequent hitters', hits that are potentially false-positive results.

A key problem in the field of high-throughput screening (HTS) is recognition of frequent hitters, which are false-positive or otherwise anomalous compounds that tend to crop up across many screens. Follow-up of such compounds constitutes a waste of resource and decreases efficiency.

We describe a systematic retrospective approach to identify anomalous hitter behavior using historical screening data. We take into account the uncertainty that arises if not enough screen data are available and extend implementation to target and technology classes.

Use of the descriptor in analyzing high-throughput screen results frees up resource for follow-up of more likely true hits in the downstream hit-deconvolution cascade, thereby increasing efficiency of screen delivery. Although effective, historical data bias can affect the annotation, and we exemplify cases where this happened.