传染病(医学专业)
抗菌剂
抗生素耐药性
重症监护医学
疾病
医学
生物
微生物学
抗生素
内科学
作者
Valerie Ortiz-Gómez,Rafael Maldonado‐Hernández
出处
期刊:ACS omega
[American Chemical Society]
日期:2025-05-20
卷期号:10 (21): 20968-20983
被引量:6
标识
DOI:10.1021/acsomega.5c01011
摘要
Antimicrobial resistance (AMR) is a growing silent pandemic driven by multidrug-resistant infections, particularly those associated with medical devices such as dental implants, heart valves, and urinary catheters. This review addresses the urgent need for alternative antimicrobial strategies by exploring the integration of artificial intelligence (AI) in the discovery of antimicrobial peptides (AMPs) and the rational design of bioactive surfaces. We describe how AI-based models accelerate the identification and optimization of peptide candidates with potent antibiofilm activity. Moreover, we examine recent advancements in surface engineering, such as biomimetic coatings, quorum sensing inhibitors, and enzyme-based strategies, that disrupt bacterial colonization and biofilm formation. The novelty of this work lies in its unified perspective that bridges computational prediction, materials science, and microbial pathogenesis to inform the next generation of antimicrobial surfaces. By highlighting innovative AI-assisted approaches and emerging hybrid strategies, this review underscores their potential to mitigate device-associated infections and address the broader challenge of AMR in healthcare settings.
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