Computational prediction and conformation of relationships among microbes, drugs and diseases

TL;DR

This paper explores how microbes, drugs, and diseases are interconnected, using computational methods to predict these relationships for better drug discovery and personalized medicine.

Contribution

The study introduces a computational approach using Cytoscape to predict and analyze microbe–drug–disease relationships.

Findings

Some predicted microbe–drug–disease relationships were validated with existing data.

The study highlights the need for further clinical investigation into unconfirmed relationships.

The findings suggest potential applications in personalized medicine and early disease diagnosis.

Abstract

Complex and diverse microbial communities are closely linked to human health, and their study plays a vital role in advancing medicine, particularly personalized healthcare. Identifying potential microbe–disease–drug relationships is useful for drug discovery and clinical treatment, and it also improves our understanding of microbial mechanisms. Due to the complexity and cost of biological experiments, computational methods provide a rapid and efficient way to predict potential interactions between microbes, drugs, and diseases. In this article, we predict relationships between microbes, drugs, and diseases using existing similarity and interaction data through Cytoscape software. Some of the potential relationships were confirmed by the available information, while the others require further clinical investigation. Due to the critical role of the microbiome in disease and medicine, more research and information are needed in this field. In the future, the various interactions between drugs, microbes, and diseases may improve the understanding of personalized medicine, promote early diagnosis, and provide potential treatments for a wide range of diseases.

The online version contains supplementary material available at 10.1038/s41598-025-29306-6.

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