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Updated: Sep 17, 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

How Artificial Intelligence Assists in Overcoming Drug Resistance?

Ferdinand Ndikuryayo¹, Xue-Yan Gong¹, Ge-Fei Hao¹

¹State Key Laboratory of Green Pesticide, Center for R&D of Fine Chemicals of Guizhou University, Guiyang, Guizhou, China.

Medicinal Research Reviews

|July 4, 2025

Summary

This summary is machine-generated.

Artificial intelligence (AI) accelerates drug discovery and combats resistance. AI offers solutions for drug resistance (DR) and pesticide resistance, but data and ethical challenges require collaboration.

Keywords:

artificial intelligence healthcare machine learning medicine resistance

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Area of Science:

Biotechnology and Pharmaceutical Sciences
Computational Biology and Bioinformatics
Public Health and Epidemiology

Background:

Rising drug resistance (DR) and pesticide resistance threaten public health.
Innovative strategies are essential for developing effective drugs and pesticides.
Artificial intelligence (AI) presents a promising approach to address these challenges.

Purpose of the Study:

To review the multifaceted roles of AI in combating drug resistance (DR).
To explore AI's application in accelerating drug and pesticide discovery.
To identify AI's potential in personalized medicine and combination therapies.

Main Methods:

Comprehensive literature analysis of AI applications in drug resistance research.
Examination of AI's role in drug discovery pipelines.
Review of AI's contribution to understanding resistance mechanisms and personalized medicine.

Main Results:

AI significantly enhances drug discovery, leading to faster development of safer medications.
AI aids in predicting and understanding mechanisms of drug and pesticide resistance.
AI-driven precision medicine offers personalized treatment strategies and identifies synergistic drug combinations.

Conclusions:

AI is a versatile tool with substantial potential to control infections and cancers in the era of drug resistance.
Addressing challenges like data accessibility and ethical considerations is crucial for AI implementation.
Interdisciplinary collaboration is vital for advancing AI-powered drug and pesticide discovery.