Home AI News AI-Driven Antibiotic Discovery Targets Deadly Drug-Resistant Bacteria

AI-Driven Antibiotic Discovery Targets Deadly Drug-Resistant Bacteria

AI-Driven Antibiotic Discovery Targets Deadly Drug-Resistant Bacteria

Researchers Use AI to Identify New Antibiotic to Combat Drug-Resistant Infections

Researchers from MIT and McMaster University have discovered a new antibiotic using artificial intelligence (AI) algorithms. The antibiotic has the potential to fight Acinetobacter baumannii, a bacteria commonly found in hospitals that causes pneumonia, meningitis, and other infections. Additionally, it is a leading cause of infections in wounded soldiers. This discovery is significant in the battle against drug-resistant bacteria, as Acinetobacter can become resistant to most antibiotics.

Fighting Superbugs with AI

To discover the new antibiotic, the researchers used a machine-learning model to evaluate approximately 7,000 different chemical compounds. The model was trained to identify compounds that could inhibit the growth of Acinetobacter baumannii. The success of this AI-driven approach supports the idea that AI can accelerate the search for novel antibiotics. It provides hope in combatting problematic pathogens such as Acinetobacter baumannii.

Drug Discovery with AI

In recent years, antibiotic-resistant bacteria have continued to grow, while the development of new antibiotics has been limited. To address this issue, MIT and McMaster University researchers turned to machine learning. In previous studies, the researchers trained a machine-learning algorithm to identify chemical structures that could inhibit growth in E. coli. This method had proved successful, leading them to focus on Acinetobacter baumannii, a major threat in multidrug-resistant bacterial infections. They exposed Acinetobacter baumannii to thousands of chemicals to identify compounds that could hinder its growth. The team selected hundreds of top compounds for experimental lab testing.

Promising Results

From the lab experiments, the team identified nine potential antibiotics, with one showing considerable potency. Surprisingly, this antibiotic, named abaucin, specifically targeted Acinetobacter baumannii and not other bacteria like Pseudomonas aeruginosa or Staphylococcus aureus. The antibiotic’s narrow spectrum is advantageous as it reduces the risk of bacteria developing resistance to the drug. It also minimizes the possibility of disrupting the gut’s beneficial bacteria, crucial for supporting the immune system.

A Novel Mechanism

The researchers further investigated how abaucin works and discovered that it interferes with lipoprotein trafficking, a process necessary for protein transportation within cells. The antibiotic specifically inhibits the function of LolE, a protein associated with this process. While lipoprotein trafficking is common in all Gram-negative bacteria, abaucin’s selectivity towards Acinetobacter baumannii suggests that variations in how this bacterium performs the process contribute to the drug’s effectiveness. The researchers are currently working to optimize the compound’s medicinal properties for potential future use in patients.

Future Applications

In addition to combating Acinetobacter baumannii, the researchers plan to apply the same AI-driven modeling approach to discover potential antibiotics for other drug-resistant infections caused by Staphylococcus aureus and Pseudomonas aeruginosa. This AI-driven discovery of a new antibiotic opens up possibilities for tackling antibiotic resistance and improving patient outcomes.

*The research was funded by the David Braley Center for Antibiotic Discovery, the Weston Family Foundation, the Audacious Project, the C3.ai Digital Transformation Institute, and many other organizations.

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