Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Genetic Screens02:46

Genetic Screens

5.4K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
5.4K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Gender shapes the relationship between productivity and journal prestige in science.

Scientific reports·2026
Same author

Comprehensive indicators and fine granularity refine density scaling laws in rural-urban systems.

Scientific reports·2026
Same author

Ground-Based Remote Sensing and Machine Learning for in Situ and Noninvasive Monitoring and Identification of Salts and Moisture in Historic Buildings.

Analytical chemistry·2025
Same author

Raw data and noise in spectrophotometry.

Analytica chimica acta·2024
Same author

Training and Match Demands of Elite Rugby Union.

Journal of strength and conditioning research·2022
Same author

A ratiometric, fluorometric approach for surface charge mapping of biosilica features.

The Analyst·2022

Related Experiment Video

Updated: Dec 5, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

8.5K

Statistical models for identifying frequent hitters in high throughput screening.

Samuel Goodwin1, Golnaz Shahtahmassebi1, Quentin S Hanley2

  • 1School of Science and Technology, Nottingham Trent University, Clifton Lane, Nottingham, NG11 8NS, UK.

Scientific Reports
|October 15, 2020
PubMed
Summary

The binomial survivor function model for frequent hitters in high throughput screening (HTS) identified too many inactive compounds. Alternative models, like the gamma distribution, provided a more accurate assessment of compound activity in drug discovery.

More Related Videos

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.6K
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
09:44

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Published on: March 3, 2015

9.8K

Related Experiment Videos

Last Updated: Dec 5, 2025

Identification of Kinase-substrate Pairs Using High Throughput Screening
11:13

Identification of Kinase-substrate Pairs Using High Throughput Screening

Published on: August 29, 2015

8.5K
Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets
06:40

Author Spotlight: Cost-Effective Transcriptomic Drug Screening - Unlocking New Targets

Published on: February 23, 2024

1.6K
High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
09:44

High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes

Published on: March 3, 2015

9.8K

Area of Science:

  • Drug discovery and development
  • Computational chemistry
  • Pharmacology

Background:

  • High throughput screening (HTS) is crucial for identifying active compounds from large libraries.
  • Existing models, like the binomial survivor function (BSF), may inaccurately classify compound activity.
  • Understanding compound behavior in HTS is essential for efficient drug discovery.

Purpose of the Study:

  • To evaluate the effectiveness of the binomial survivor function (BSF) model for identifying frequent hitters in HTS.
  • To investigate alternative statistical models for characterizing compound behavior in HTS data.
  • To analyze the implications of disproportionate compound retesting on drug discovery strategies.

Main Methods:

  • Analysis of 872 publicly available HTS datasets.
  • Assessment of the binomial survivor function (BSF) model.
  • Investigation of generalized logistic, gamma, and negative binomial distributions as alternative models.
  • Evaluation of compound testing frequency and its impact on results.

Main Results:

  • The BSF model identified a large proportion of 'infrequent hitters,' leading to its rejection for frequent hitter identification.
  • The gamma model reduced the proportion of both frequent and infrequent hitters compared to the BSF.
  • Disproportionate retesting (≥300 times for 17.6% of compounds) dominated the datasets, suggesting compound repurposing over novel drug discovery.
  • Individual compounds were poorly characterized due to limited testing, while assays were well-characterized by numerous compounds.

Conclusions:

  • The BSF model is inadequate for identifying frequent hitters in HTS.
  • Alternative models, such as the gamma distribution, offer improved characterization of compound behavior.
  • Extensive retesting in HTS datasets may represent large-scale compound repurposing rather than early-stage drug discovery.
  • The current HTS approach poorly characterizes individual compounds, necessitating a re-evaluation of drug discovery strategies.