Penn researchers develop predictive AI model for antibiotic discovery
Penn researchers develop predictive AI model for antibiotic discovery
Publish Date: 2026-06-15 23:32:00
Source Domain: www.thedp.com
Researchers at the School of Engineering and Applied Science have developed an artificial intelligence-powered model that examines and improves antibiotic candidates.
The model, called ApexGO, builds upon a model which was released two years ago and tells researchers whether a given peptide is likely to have antimicrobial properties. The latest iteration goes beyond analyzing a set of molecules and suggests improvements to make more effective antibiotics.
Presidential Associate Professor César de la Fuente characterized ApexGO — which stands for APEX generative optimization — as “a way of improving lousy antimicrobials” in an interview with The Daily Pennsylvanian.
“I think 85% of everything that it creates that we synthesized was capable of killing bacteria in real-world experiments,” de la Fuente said, adding that “72% of the variants that it created were better than the template molecule at killing bacteria.”
Research assistant professor of psychiatry Marcelo Torres wrote in a statement to the DP that the model “learns patterns from biological sequences and uses this information to prioritize candidates that are more likely to have antibacterial activity.”
Get the DP delivered straight to your inbox.
Sign Up
“Instead of testing molecules randomly, the system balances what it predicts to be promising with areas where the model is still uncertain,” Torres wrote. “This helps the platform identify candidates that are both potentially active and informative for improving the search.”
2025 Engineering School Ph.D. graduate Natalie Maus explained that, because there are so many possible peptides that could have antibiotic properties, “you could do wet lab experiments all day every day for a year, and you would be testing such a small fraction of the space of possibilities.”
She explained that the ApexGO model…
