Researchers from McMaster University and Stanford University have developed an AI generative model that can rapidly design effective small-molecule antibiotics against Acinetobacter baumannii. Antimicrobial resistance (AMR) poses a global public health threat, causing millions of deaths each year. The study highlights the potential of AI in creating innovative solutions to combat AMR.
A new study by researchers at McMaster University and Stanford University demonstrates how an AI generative model can quickly create innovative small-molecule antibiotic candidates to combat Acinetobacter baumannii. Antimicrobial resistance (AMR) is a global public health threat, resulting from bacteria, viruses, fungi, and parasites developing resistance to drugs over time. In 2019, AMR contributed to 4.95 million deaths worldwide, with 1.
27 million directly caused by bacterial AMR, according to WHO. The CDC estimates over 2.8 million antimicrobial infections in the U.S., leading to 35,000 deaths annually
AI Antibiotics Antimicrobial Resistance Acinetobacter Baumannii Public Health Research Mcmaster University Stanford University
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