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Related Concept Videos

Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
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Updated: May 31, 2026

An Automated T-maze Based Apparatus and Protocol for Analyzing Delay- and Effort-based Decision Making in Free Moving Rodents
07:42

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Published on: August 2, 2018

HTS promiscuity analyses for accelerating decision making.

Alexander Böcker1, Pierre R Bonneau, Paul J Edwards

  • 1Evotec AG, Hamburg, Germany.

Journal of Biomolecular Screening
|June 18, 2011
PubMed
Summary
This summary is machine-generated.

This study identifies frequent hitters in high-throughput screening (HTS) data and develops models to predict promiscuous compounds. This approach streamlines hit discovery and reduces costs by understanding compound behavior in assays.

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Area of Science:

  • Drug Discovery
  • Computational Chemistry
  • Assay Development

Background:

  • Frequent hitters are compounds identified across multiple high-throughput screening (HTS) assays.
  • Their promiscuous behavior can stem from specific target interactions or unspecific properties like reactivity.
  • Understanding these behaviors is crucial for efficient hit profiling and assay interpretation.

Purpose of the Study:

  • To develop methods for mining large HTS data repositories to identify frequent hitters.
  • To gain insights into the causes of compound promiscuity in HTS assays.
  • To build predictive models for identifying potentially promiscuous compounds.

Main Methods:

  • Data mining of large-scale HTS repositories.
  • Analysis of compound behavior across diverse assays.
  • Development of predictive models for promiscuity.
  • Application to recent large-scale HTS datasets.

Main Results:

  • Successful identification of frequent hitters within a large HTS dataset.
  • Characterization of factors contributing to compound promiscuity.
  • Demonstrated utility of predictive models in two case studies.
  • Validation of the approach using real-world HTS data.

Conclusions:

  • The developed approach effectively mines HTS data to identify and predict frequent hitters.
  • This strategy provides valuable insights into compound behavior and assay performance.
  • The methods accelerate decision-making in hit discovery, reducing time and cost.