Exploiting the fitness cost of metallo-β-lactamase expression can overcome antibiotic resistance in bacterial pathogens.

Carbapenems are last-resort antibiotics for treating bacterial infections. The widespread acquisition of metallo-β-lactamases, such as VIM-2, contributes to the emergence of carbapenem-resistant pathogens, and currently, no metallo-β-lactamase inhibitors are available in the clinic. Here we show that bacteria expressing VIM-2 have impaired growth in zinc-deprived environments, including human serum and murine infection models.

Machine learning in preclinical drug discovery.

Drug-discovery and drug-development endeavors are laborious, costly and time consuming. These programs can take upward of 12 years and cost US $2.5 billion, with a failure rate of more than 90%.

Deep learning-guided discovery of an antibiotic targeting Acinetobacter baumannii.

Acinetobacter baumannii is a nosocomial Gram-negative pathogen that often displays multidrug resistance. Discovering new antibiotics against A. baumannii has proven challenging through conventional screening approaches.