APEX is an AI model that predicts whether or not a given peptide is likely to have antimicrobial properties. Now, APEXGo can identify antibiotic candidates with laboratory activity against disease-causing bacteria, simply by searching large datasets.
2 min. read
Researchers at Penn’s School of Engineering and Applied Science have developed ApexGO, a novel, AI-powered method for turning promising but imperfect antibiotic candidates into more potent ones.
Unlike many existing AI approaches to antibiotic discovery, which screen large databases for molecules that might work, ApexGO starts with a small number of imperfect candidates and improves them step by step, using a predictive algorithm to evaluate each modification and guide the next.
(From left) Co-authors Jacob R. Gardner, César de la Fuente and Marcelo Torres, holding a 3D-printed example of the kind of antibiotic peptide they generated using AI.
(Image: Sylvia Zhang)
“Antibiotic discovery is fundamentally a search problem across an enormous molecular space. ApexGO gives us a way to navigate that space with far more direction,” says César de la Fuente, Presidential Associate Professor in Psychiatry and Microbiology in the Perelman School of Medicine, in bioengineering and in chemical and biomolecular engineering in the School of Engineering and Applied Science, and in chemistry in the School of Arts & Sciences, and co-senior author of a new paper describing the method in Nature Machine Intelligence.
“APEX helped us find promising antibiotic candidates in enormous biological datasets,” says Marcelo Torres, research assistant professor of psychiatry in the Perelman School of Medicine and co-first author of the paper, referring to work that revealed antibiotic candidates everywhere from woolly mammoths to giant sloths. “ApexGO takes the next step: once we have a promising molecule, it helps us ask how to make it better.”
“ApexGO begins with a promising but imperfect peptide,” explains de la Fuente. Referring to a short string of amino acids, he says, “proposes precise edits, predicts whether those changes are likely to enhance antimicrobial activity, and then keeps moving toward versions that are more likely to work when we make and test them.”
Essentially, one part of ApexGO—short for APEX Generative Optimization—suggests molecular tweaks, while the previously published APEX model predicts whether those changes are likely to increase antimicrobial activity. ApexGO then uses those predictions to guide the next round of proposed edits.
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