Statistical models for identifying frequent hitters in high throughput screening

Goodwin, S., Shahtahmassebi, G. ORCID: 0000-0002-0630-2750 and Hanley, Q.S. ORCID: 0000-0002-8189-9550, 2020. Statistical models for identifying frequent hitters in high throughput screening. Scientific Reports, 10: 17200. ISSN 2045-2322

1370240_Hanley.pdf - Published version

Download (2MB) | Preview


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) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

Item Type: Journal article
Publication Title: Scientific Reports
Creators: Goodwin, S., Shahtahmassebi, G. and Hanley, Q.S.
Publisher: Nature Research (part of Springer Nature)
Date: 14 October 2020
Volume: 10
ISSN: 2045-2322
Rights: © The Author(s) 2020. Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit
Divisions: Schools > School of Science and Technology
Record created by: Linda Sullivan
Date Added: 01 Oct 2020 09:43
Last Modified: 31 May 2021 15:14

Actions (login required)

Edit View Edit View


Views per month over past year


Downloads per month over past year