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Updated: Sep 2, 2025

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations

Published on: July 24, 2021

Computational models, databases and tools for antibiotic combinations.

Ji Lv^1,2, Guixia Liu^1,2, Junli Hao³

¹College of Computer Science and Technology, Jilin University, Changchun, China.

Briefings in Bioinformatics

|August 1, 2022

Summary

This summary is machine-generated.

Computational models can predict effective antibiotic combinations to combat antimicrobial resistance. This review summarizes current models, data resources, and tools, aiding biologists in developing better predictive strategies.

Keywords:

antibiotic combinations complex network databases machine learning synergy effect

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Last Updated: Sep 2, 2025

Quadruple-Checkerboard: A Modification of the Three-Dimensional Checkerboard for Studying Drug Combinations

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Published on: July 24, 2021

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Published on: May 21, 2018

Area of Science:

Computational biology
Antimicrobial resistance research
Drug discovery

Background:

Antimicrobial resistance (AMR) is a growing global health threat.
Antibiotic combinations are crucial for extending antibiotic efficacy and combating AMR.
Traditional screening methods for antibiotic combinations are inefficient.

Purpose of the Study:

To review existing computational models for predicting antibiotic combinations.
To identify limitations and challenges in current computational approaches.
To compile available data resources and tools for antibiotic combination research.

Main Methods:

Systematic literature review of computational models for antibiotic combinations.
Analysis of reported data resources and tools.
Discussion of model accuracy, interpretability, and future directions.

Main Results:

A comprehensive overview of various computational models used in antibiotic combination prediction.
Identification of key challenges including data scarcity and model generalizability.
A curated list of relevant data resources and computational tools.

Conclusions:

Computational models offer a promising avenue to accelerate the discovery of effective antibiotic combinations.
Further development is needed to enhance model accuracy and interpretability for practical application.
This review provides a valuable resource for researchers aiming to advance computational strategies against AMR.