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Genetic Screens

Genetic Screens

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...

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Updated: Apr 18, 2026

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

High-throughput Screening for Small-molecule Modulators of Inward Rectifier Potassium Channels

Published on: January 27, 2013

Introducing the 'active search' method for iterative virtual screening.

Roman Garnett¹, Thomas Gärtner, Martin Vogt

¹Institute of Computer Science III, Rheinische Friedrich-Wilhelms-Universität Bonn, Römerstr. 164, 53117, Bonn, Germany.

Journal of Computer-Aided Molecular Design

|February 1, 2015

Summary

This summary is machine-generated.

This study introduces an active search method for efficiently finding active compounds. It uses Bayesian decision theory to optimize compound selection in iterative virtual screening, improving discovery success.

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

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Published on: March 3, 2015

Area of Science:

Computational chemistry
Cheminformatics
Drug discovery

Background:

Traditional virtual screening methods often use myopic ranking strategies.
Identifying active compounds requires efficient exploration of large chemical spaces.

Purpose of the Study:

To develop and evaluate a novel sequential similarity searching method for active compounds.
To improve the efficiency and accuracy of virtual compound screening.

Main Methods:

Introduced an 'active search' approach based on Bayesian decision theory.
Implemented an exploratory active search as a less-myopic strategy.
Conducted iterative virtual screening trials on 120 compound classes.

Main Results:

The active search approach optimally ranks compounds by observing iterative outcomes.
Exploratory active search demonstrated accurate identification of active compounds.
Validated performance across diverse compound classes.

Conclusions:

The active search method offers a more optimal strategy than myopic approximations.
This approach enhances the identification of active compounds in virtual screening.
Freely available source code and data facilitate further research and application.