Systems Biology, Multi-Omics, and Artificial Intelligence Paradigms in Bacterial Mutational Resistance and De Novo Antibacterial Drug Design
TL;DR
The convergence of high-throughput multi-omics profiling spanning genomics, transcriptomics, proteomics, and metabolomics with advanced artificial intelligence (AI) and machine learning (ML) architectures offers a powerful framework for deciphering the complex molecular underpinnings of resistance and accelerating target-directed drug discovery.
Abstract
Antimicrobial resistance (AMR) represents one of the most critical global threats to public health; it was associated with an estimated 4.95 million deaths in 2019 [1] and is projected to claim up to 10 million lives annually by 2050 if no effective interventions are implemented [2]. The rapid dissemination of multidrug-resistant (MDR) bacterial strains continues to render first-line and last-resort antibiotics ineffective, outpacing traditional drug discovery pipelines. The rapid dissemination of multidrug-resistant (MDR) bacterial strains continues to render first-line and last-resort antibiotics ineffective, outpacing traditional drug discovery pipelines [3]. Historically, the development of new antibacterial agents relied on modifying existing chemical classes, a strategy that is increasingly vulnerable to rapid selection of resistance under clinical pressure [4]. To overcome this bottleneck, the modern research paradigm is shifting from single-molecule investigations to systems-level analyses [5]. The convergence of high-throughput multi-omics profiling spanning genomics, transcriptomics, proteomics, and metabolomics with advanced artificial intelligence (AI) and machine learning (ML) architectures offers a powerful framework for deciphering the complex molecular underpinnings of resistance and accelerating target-directed drug discovery [6].