Paving AI Routes to Fight Resistant Infections: Fusing Experimental and Computational Efforts

The emergence of new infection and drug resistance has become one of the greatest threats to global health, demanding radical innovation in our research and groundbreaking therapeutic strategies and diagnostic. The integration of lab results and AI methodologies has been proposed as highly beneficial towards the identification of microbes and of new antimicrobials in a broad spectrum of biological and chemical sources. Also, AI models can help elucidate the susceptibility endpoint parameters of the peptides towards specific pathogens.
Ultimately, AI-informed pipelines aim to identify candidates with enhanced properties exhibiting relevant activities and minimal toxicity to human cells.

This research topic focuses on the application of artificial intelligence to combat antimicrobial resistance and deepen the knowledge of the biology of infections. We seek research and reviews that present tangible, AI+wet lab fused solutions in the fight against superbugs: from the algorithmic design and discovery of innovative antimicrobials (such as multifunctional peptides or drug-peptide hybrids) to the optimisation of treatment combinations, reduction of toxicity through computational models and the identification of microbes for diagnostic purposes.

We invite contributions that showcase hybrid workflows integrating in vitro, in vivo and in silico approaches for accelerated, translational antimicrobial pipelines. Research including but not limited to:
• Bioinformatics approaches for screening innovative peptide reservoirs, e.g. peptidomes from extinct organisms and human defensins, microbiome and secretome.
• AI-guided identification and design of new antimicrobial peptides (AMPs) and peptide-drug hybrids, leveraging deep learning and LLMs to mine sequence-function relationships and expand innovative peptide libraries.
• Machine learning-based pathogen identification technology and laboratory automation.
• Optimization of functional drug and peptide properties—such as toxicity, selectivity, and efficacy—using machine learning-driven predictive modeling and multi-objective optimization.
• In silico discovery and prioritization of synergistic combinations (e.g., peptide-antibiotic, peptide-peptide) effective against multi-resistant microbial strains.
• Development and integration of AI-driven pipelines for multi-omics data analysis in the context of infection modelling, therapeutic targeting and diagnostic.
• Automated mining of biomedical literature to predict novel uses of existing compounds against resistant pathogens.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Systematic Review

Keywords: resistant infection, multifunctional compounds, combinatorial strategies, functional optimization, machine learning, peptide therapeutics, deep learning, large language models, AI

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.