the role of artificial intelligence in the development of efflux pump inhibitors

Background: antimicrobial resistance (amr) mediated by efflux pumps constitutes a critical health problem, necessitating urgent strategies for the development of new efflux pump inhibitors (epis). in this regard, artificial intelligence (ai) seems to be an innovative strategy for accelerating discovery, optimization, and understanding of epis mechanisms of action.conclusion: this review summarizes recent advances regarding the role of ai in the development of new epi, with emphasis on machine learning (ml) based inhibitor prediction, molecular dynamics (md) for binding analysis, and quantitative structure-activity relationship modeling (qsar). by regrouping data from recent studies, we discuss here the important role played by ai in the improvement of lead identification, inhibitor designs, and the study of the resistance mechanisms. despite current limitations such as limited, fragmented data and structural complexity of efflux pumps, ai offers great promise to revolutionize epi development. in order to effectively combat amr, we address here some key approaches, applications, challenges, and future directions, demonstrating the urgent need for interdisciplinary collaboration.