Researchers at the Perelman School of Medicine at the University of Pennsylvania have used artificial intelligence (AI) to accelerate the discovery of new antibiotics. The team, co-led by César de la Fuente, used machine learning to analyze genomic data from tens of thousands of bacteria and other organisms, identifying nearly one million potential antibiotic compounds. Dozens of these showed promising results against disease-causing bacteria in initial tests. The study demonstrates the potential of AI in antibiotic discovery, providing new leads for developers and marking a new era in the field. The team’s findings are freely available in their AMPSphere repository.
Harnessing AI for Antibiotic Discovery
The discovery of antibiotics nearly a century ago revolutionized medicine, with natural bacteria-killing abilities of microbes being harnessed to create life-saving drugs. Today, researchers at the Perelman School of Medicine at the University of Pennsylvania are suggesting that the discovery of natural-product antibiotics is set to accelerate into a new era, powered by artificial intelligence (AI).
In a study published in Cell, the researchers utilized a form of AI known as machine learning to search for antibiotics in a vast dataset containing the recorded genomes of tens of thousands of bacteria and other primitive organisms. This effort yielded nearly one million potential antibiotic compounds, with dozens showing promising activity in initial tests against disease-causing bacteria.
César de la Fuente, PhD, a Presidential Assistant Professor in Psychiatry, Microbiology, Chemistry, Chemical and Biomolecular Engineering, and Bioengineering, and co-senior author of the study, emphasized the significant acceleration in the discovery of new candidate drugs due to AI. What once took years can now be achieved in hours using computers.
Mining the Microbial World for Antibiotics
Nature has always been a rich source of new medicines, especially antibiotics. Bacteria, ubiquitous on our planet, have evolved numerous antibacterial defenses, often in the form of short proteins or peptides that can disrupt bacterial cell membranes and other critical structures. The growing threat of antibiotic resistance has underscored the urgent need for new antimicrobial compounds.
In recent years, de la Fuente and colleagues have pioneered AI-powered searches for antimicrobials. They have identified preclinical candidates in the genomes of contemporary humans, extinct Neanderthals and Denisovans, woolly mammoths, and hundreds of other organisms. One of the lab’s primary goals is to mine the world’s biological information for useful molecules, including antibiotics.
AI-Powered Exploration of Microbial Genomes
For this new study, the research team used a machine learning platform to sift through multiple public databases containing microbial genomic data. The analysis covered 87,920 genomes from specific microbes as well as 63,410 mixes of microbial genomes—“metagenomes”—from environmental samples. This comprehensive exploration spanned diverse habitats around the planet.
This extensive exploration succeeded in identifying 863,498 candidate antimicrobial peptides, more than 90 percent of which had never been described before. To validate these findings, the researchers synthesized 100 of these peptides and tested them against 11 disease-causing bacterial strains, including antibiotic-resistant strains of E. coli and Staphylococcus aureus.
Promising Results and Future Directions
Initial screening revealed that 63 of these 100 candidates completely eradicated the growth of at least one of the pathogens tested, and often multiple strains. In some cases, these molecules were effective against bacteria at very low doses. Promising results were also observed in preclinical animal models, where some of the potent compounds successfully stopped infections.
Further analysis suggested that many of these candidate molecules destroy bacteria by disrupting their outer protective membranes, effectively popping them like balloons. The identified compounds originated from microbes living in a wide variety of habitats, including human saliva, pig guts, soil and plants, corals, and many other terrestrial and marine organisms. This validates the researchers’ broad approach to exploring the world’s biological data.
The findings demonstrate the power of AI in discovering new antibiotics, providing multiple new leads for antibiotic developers, and signaling the start of a promising new era in antibiotic discovery. The team has published their repository of putative antimicrobial sequences, which they call AMPSphere, which is open access and freely available.
The Team and Funding
The research was co-authored by a team of international scientists and supported by various funding bodies including the National Key R&D Program of China, the National Natural Science Foundation of China, Shanghai Science and Technology Innovation Fund, Shanghai Municipal Science and Technology Major Project, The Science and Technology Commission of Shanghai Municipality, The Australian Research Council, the AIChE Foundation, the National Institutes of Health, the Defense Threat Reduction Agency, and the European Union’s Horizon 2020 research and innovation program.
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