Research into microbial pathogenesis and antimicrobial resistance (AMR) is at a critical juncture. Traditional experimental methods often struggle with the complexity and scale of multi-omics data, hindering progress in understanding host-pathogen interactions and the rapid evolution of resistance. Machine learning (ML) and artificial intelligence (AI) are poised to revolutionize this field by enabling the analysis of vast, heterogeneous datasets to uncover hidden patterns, predict emergent properties, and generate testable hypotheses. These computational approaches can decipher molecular mechanisms of virulence, forecast resistance evolution, accelerate antimicrobial discovery, and power next-generation diagnostic systems, heralding a new era of intelligent, data-driven infectious disease research.

Research into microbial pathogenesis and antimicrobial resistance (AMR) is at a critical juncture. Traditional experimental methods often struggle with the complexity and scale of multi-omics data, hindering progress in understanding host-pathogen interactions and the rapid evolution of resistance. Machine learning (ML) and artificial intelligence (AI) are poised to revolutionize this field by enabling the analysis of vast, heterogeneous datasets to uncover hidden patterns, predict emergent properties, and generate testable hypotheses. These computational approaches can decipher molecular mechanisms of virulence, forecast resistance evolution, accelerate antimicrobial discovery, and power next-generation diagnostic systems, heralding a new era of intelligent, data-driven infectious disease research.

This Research Topic aims to showcase cutting-edge research at the intersection of computational science and microbiology. We seek to highlight innovative applications of ML and AI that provide novel insights into microbial pathogenesis and AMR dynamics. Our goal is to foster a cross-disciplinary dialogue that bridges computational innovation with biological validation. Specifically, we encourage contributions that: 1) Decipher the molecular mechanisms of pathogen-host interactions to identify key virulence factors; 2) Develop high-precision models for predicting resistance phenotypes and evolutionary trends from genomic and clinical data; 3) Leverage AI for the rational design of novel anti-infective agents and resistance-breaking strategies; and 4) Establish intelligent frameworks for rapid pathogen detection and AMR surveillance to guide clinical decision-making.

We welcome the submission of Original Research, Reviews, Mini-Reviews, Perspectives, and Methods articles that demonstrate significant technological innovation and biological insight. Key areas of interest include, but are not limited to:

• Pathogenic Mechanism Analysis: AI-driven modeling of host-pathogen interactions, prediction of virulence factors, and analysis of infection dynamics.

• Antimicrobial Resistance (AMR) Research: ML-assisted mining of resistance genes, prediction of resistance phenotypes from genotypic data, and evolutionary trajectory modeling.

• Intelligent Drug Development: AI-aided design and discovery of antimicrobial peptides, small molecules, and compounds that overcome existing resistance mechanisms.

• Diagnostic Technology Optimization: Development of AI-powered tools for rapid pathogen identification, antimicrobial susceptibility testing, and resistance gene detection in clinical samples.

We are particularly interested in studies that integrate multi-omics data, develop explainable AI models for biological discovery, and present clinically translatable solutions against priority pathogens, such as the ESKAPE group. This collection will serve as a comprehensive platform for advancing the intelligent transformation of microbiology and anti-infective therapy.

Are you interested in contributing a manuscript to this Research Topic?

Machine Learning and AI-Driven Insights into Microbial Pathogenesis and Drug Resistance