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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 result in visible changes...

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vScreenML v2.0: Improved Machine Learning Classification for Reducing False Positives in Structure-Based Virtual

Grigorii V Andrianov^1,2, Emeline Haroldsen¹, John Karanicolas^1,3

¹Cancer Signaling & Microenvironment Program, Fox Chase Cancer Center, Philadelphia, PA 19111, USA.

International Journal of Molecular Sciences

|November 27, 2024

Summary

This summary is machine-generated.

vScreenML 2.0 enhances virtual screening by improving hit-finding rates. This new Python tool offers better usability and accuracy than previous versions and other methods for drug discovery.

Keywords:

drug discovery machine learning virtual screening

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Area of Science:

Computational chemistry
Drug discovery
Machine learning

Background:

Traditional virtual screening methods often yield low hit rates, with many predicted compounds failing to interact with target proteins.
Make-on-demand chemical libraries are increasingly used, necessitating more accurate virtual screening tools.
Previous machine learning tools like vScreenML showed promise but suffered from usability issues and software dependencies.

Purpose of the Study:

To introduce vScreenML 2.0, a significantly improved version of the vScreenML virtual screening tool.
To address the limitations of the original vScreenML, focusing on enhanced usability and streamlined implementation.
To provide a more effective computational method for identifying potential drug candidates.

Main Methods:

Development of vScreenML 2.0 as a streamlined Python implementation.
Comparative benchmarking against other widely used virtual screening tools.
Evaluation of hit-finding discovery rates and accuracy.

Main Results:

vScreenML 2.0 demonstrates superior performance in virtual screening hit discovery compared to existing tools.
The new implementation offers improved usability and removes dependencies on obsolete or proprietary software.
Benchmarks confirm enhanced accuracy in identifying compounds likely to engage target proteins.

Conclusions:

vScreenML 2.0 represents a significant advancement in computational drug discovery, offering improved hit-finding efficiency.
The tool's enhanced usability and Python-based implementation facilitate broader adoption in virtual screening workflows.
vScreenML 2.0 provides a more reliable and accessible method for accelerating the identification of novel therapeutic agents.