Statistical models for identifying frequent hitters in high throughput screening.

ABSTRACT: High throughput screening (HTS) interrogates compound libraries to find those that are "active" in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of "frequent hitters" using 872 publicly available HTS data sets. We found large numbers of "infrequent hitters" using this model leading us to reject the BSF for identifying "frequent hitters." As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1-1613 times) an

SUBMITTER: Goodwin S 

PROVIDER: S-EPMC7560657 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature