Using machine learning, scientists at the University of Pennsylvania have identified nearly a million compounds that could become the basis for new antimicrobial drugs.
Antibiotic-resistant infections kill more than a million people a year. The WHO predicts that by 2050, the number of deaths could rise to 10 million.
Previously, scientists studied the effectiveness of antimicrobials using traditional methods, such as testing water and soil samples. It took years to find and create drugs. Artificial intelligence can significantly speed up the process.
💥 How Does it Work?
Scientists used AI to analyze huge datasets, containing the genomes of tens of thousands of microorganisms. Looking for DNA fragments that might have antimicrobial activity, researchers selected around 900,000 antimicrobial peptides. At least 79% of these fell into the “candidates for antibiotics” category, meaning they have the potential to kill at least one pathogen. In the laboratory, scientists synthesized 100 of these peptides and tested them on 11 dangerous bacterial pathogens, including antibiotic-resistant strains of Escherichia coli and Staphylococcus aureus.
AI in antibiotic discovery is now a reality and has significantly accelerated our ability to discover new candidate drugs. What once took years can now be achieved in hours using computers.
César de la Fuente, PhD, study co-author
The results of the study are published in the journal Cell.
🆕 In the history of medicine, this is the most promising effort to find new drugs. In 2020, scientists at the Massachusetts Institute of Technology discovered, with the help of AI, the first new antibiotics in 30 years.
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How AI Is Pioneering New Drug Discoveries
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