ABSTRACT
Emerging infections, from novel or well-known pathogens that develop drug resistance, are a major threat to public health. Current antimicrobial drug discovery involves screening large libraries of small organic molecules for a vanishingly small number of potential hits requiring years of expensive in vitro, pre-clinical, and clinical testing that may still fail. Recent work indicates that artificial intelligence (AI) and machine learning (ML) can accelerate this process. However, the current paradigm suffers two major limitations that preclude its ability to meet the needs of rapidly emerging infections. First, "black box" AI fails to reveal fundamental mechanisms of biological activity. Second, the search space for potential drug candidates cannot be limited to traditional organic molecules and biologics. Despite decades of screening these chemicals libraries, there have only been 1-2 new classes of antibiotics. To address these limitations, we look beyond current molecular targeting paradigms to include a vast space of inorganic nanoparticles (NPs). Our group has contributed to a body of research that indicates that carefully engineered NPs can form predictable lock-and-key complexes with biomolecules and serve as an entirely new source of antimicrobial agents. We have also contributed to work demonstrating that NP-biomolecule interactions can be accurately predicted using molecular dynamics and ML models. In this presentation I will discuss some examples of how NP-biomolecule interactions occur and ultimately result in antimicrobial function. I will also provide a framework for future development and discovery of antimicrobial NPs.