Decoding Antimicrobial Resistance: Bridging Genomics, Structural Biology, AI and Experimental Validation

Antimicrobial resistance (AMR) poses a major and growing threat to global health. Recent advances in pathogen informatics, structural bioinformatics, cheminformatics, artificial intelligence (AI) have created new opportunities to investigate the molecular determinants of AMR. This Research Topic aims to advance our understanding of the molecular mechanisms underlying antimicrobial resistance by bridging pathogen genomics, bioinformatics, AI-driven approaches along with experimental investigations. Emphasis will be placed on studies that establish meaningful relationships between pathogen genetic variation, molecular and structural alterations, and experimentally observed resistance phenotypes.The scope encompasses antimicrobial resistance in bacterial, fungal and viral pathogens. A central focus is the development, integration and/or application of large-scale pathogen genomic/proteomic/three-dimensional structural datasets to investigate the molecular determinants of resistance. We particularly encourage interdisciplinary studies that bridge computational innovation with experimental evidence and provide mechanistic insights into antimicrobial resistance. Where new experimental validation is not feasible, computational studies supported by well-curated, publicly available experimental datasets will also be considered.This Research Topic welcomes contributions addressing, but not limited to, the following areas:• Predicting AMR using AI: Predicting resistance-associated mutations and their molecular consequences or predicting AMR in pathogen sequences/strains using machine learning, AI or protein language models. • Computational and experimental integration: Validating computationally predicted resistance markers through biochemical, biophysical, microbiological or other experimental approaches.• AMR databases/resources/tools: Developing computational tools, resources or databases dedicated to AMR. Resources linked with publicly available experimental data would be of special interest.• Molecular and structural mechanisms of resistance: Investigating mutations involved in AMR/ pathogen’s resistance phenotype, their mutational landscapes and impacts on protein structure and function.• Protein–ligand and protein–protein interactions: Understanding how resistance-associated mutations influence molecular recognition, drug binding and antimicrobial activity, using innovative approaches. Topic editors: Vaishali Waman, Mitul Srivastava Source: https://www.frontiersin.org/research-topics/87568/decoding-antimicrobial-resistance-bridging-genomics-structural-biology-ai-and-experimental-validation