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Notably, 70% of these deaths are due to gram-negative pathogens, and no new classes of gram-negative-active antibiotics have been approved by the US Food and Drug Administration in the past 55 years. The challenges of converting compounds with\n                    <jats:italic toggle=\"yes\">in vitro<\/jats:italic>\n                    biochemical activity to whole cell gram-negative antibacterial activity are significant, as the outer membrane and promiscuous efflux pumps thwart the potential of most antibiotic candidates. Significant strides have been made toward understanding compound penetration and accumulation in gram-negative bacteria, but efflux remains a major obstacle for antibiotic drug discovery. Recent advances in machine learning (ML) algorithms and increased accessibility of code and programs for the nonexpert suggest artificial intelligence could help address the efflux problem. Here, we discuss work toward understanding efflux and cast a vision for how ML can be utilized to address compound efflux from gram-negative bacteria.\n                  <\/jats:p>","DOI":"10.1089\/mdr.2024.0195","type":"journal-article","created":{"date-parts":[[2025,2,13]],"date-time":"2025-02-13T10:00:34Z","timestamp":1739440834000},"page":"444-456","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Understanding Compound Efflux from Gram-Negative Bacteria, a Final Frontier for Antibiotic Discovery"],"prefix":"10.1177","volume":"32","author":[{"given":"Rebecca J.","family":"Ulrich","sequence":"first","affiliation":[{"name":"Department of Chemistry and Carl R. 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