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Related Experiment Video

Updated: Aug 11, 2025

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Published on: December 9, 2015

Bioinformatics toolbox for exploring target mutation-induced drug resistance.

Yuan-Qin Huang¹, Ping Sun¹, Yi Chen¹

¹National Key Laboratory of Green Pesticide, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Guizhou University, Guiyang 550025, P. R. China.

Briefings in Bioinformatics

|February 4, 2023

Summary

This study surveys 59 bioinformatics tools to combat drug resistance caused by target mutations. It provides a comparative analysis to aid researchers in selecting effective methods for drug resistance prediction.

Keywords:

in silico database drug resistance mutation protein–ligand affinity web server

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Published on: December 9, 2015

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Area of Science:

Bioinformatics
Drug Discovery
Genomics

Background:

Drug resistance poses a significant threat to human health, agriculture, and food security.
Target mutation-induced drug resistance is a critical area of biological research.
Existing bioinformatics tools for studying drug resistance are scattered and lack systematic comparison.

Purpose of the Study:

To systematically survey and analyze freely available bioinformatics tools for exploring target mutation-induced drug resistance.
To compare the strengths and limitations of these tools.
To provide a practical resource for researchers and non-specialists.

Main Methods:

Systematic survey of 59 freely available bioinformatics tools.
Analysis based on functionality, data volume, data source, operating principle, and performance.
Comparative discussion of tool strengths, limitations, and application examples.
Clinical perspective evaluation of predictive tools.

Main Results:

A comprehensive overview of 59 bioinformatics tools for drug resistance research.
Detailed analysis of tool characteristics and performance metrics.
Identification of strengths and limitations for each tool.
Practical insights from a clinical viewpoint.

Conclusions:

The surveyed tools offer cost-effective, fast, and effective methods for elucidating drug resistance mechanisms.
A systematic comparison is crucial for optimal tool selection.
This work serves as a valuable toolbox for diverse scientific fields and aids in understanding drug resistance prediction.