Penn researchers create AI tool to speed antibiotic discovery

Researchers at the University of Pennsylvania 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.

“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 Bioengineering and in Chemical and Biomolecular Engineering in the School of Engineering and Applied Science, in Psychiatry and Microbiology in the Perelman School of Medicine and in Chemistry in the School of Arts & Sciences, and co-senior author of a new paper describing the method in Nature Machine Intelligence. 

“ApexGO begins with a promising but imperfect peptide,” explains de la Fuente, referring to a short string of amino acids, “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.”

Laboratory tests against disease-causing bacteria supported ApexGO’s predictions: 85% of the AI-generated molecules halted bacterial growth, while 72% outperformed the peptides from which they were derived. In mice, two antimicrobial peptides created by ApexGO reduced bacterial counts at levels comparable to polymyxin B, an FDA-approved antibiotic used as a last-resort treatment for some drug-resistant infections.

“What is striking is that ApexGO’s predictions held up in the real world,” says Jacob R. Gardner, Assistant Professor in Computer and Information Science (CIS) and the paper’s other senior co-author. “ApexGO was optimizing against another computer model, so one concern was that it might find molecules that looked good to the model but failed in the lab. Instead, the majority of the molecules it designed actually worked.”

From Screening Molecules to Making New Ones

For years, the de la Fuente lab has looked for antibiotic candidates in unlikely places, from frog secretions to ancient microbes. Two years ago, the group released APEX, an AI model that predicts whether or not a given peptide is likely to have antimicrobial properties. 

“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.” 

That’s where Gardner’s lab comes in. The group specializes in methods like Bayesian optimization, which helps AI systems explore large numbers of possible solutions efficiently. “It would be impossible to test every possible peptide,” says Yimeng Zeng, a doctoral student in CIS and co-first author of the paper. “Bayesian optimization helps the model make informed choices about what to try next, balancing candidates that look promising with candidates that could teach the model something new.”

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. “If a region of the search space looks promising, the model can spend more effort exploring nearby variants,” says Zeng. “But it can also move into less certain regions, where there may still be hidden improvements.”

Searching More Systematically 

Until now, the researchers point out, antibiotics have largely been found by accident. The most famous example is also the first: penicillin, which Alexander Fleming discovered after noticing that mold in a petri dish was restricting the growth of bacteria. “In a sense, we’ve been incredibly lucky,” says de la Fuente. “ApexGO points to a more systematic way forward.” 

The space of all possible antimicrobial peptides is huge: Like searching a vast forest for something small or rare, finding an antibiotic peptide is normally prohibitively time-consuming. Even a short peptide can be modified in an enormous number of ways, making it impossible for researchers to synthesize and test every possible version by hand.

That ApexGO could identify antibiotic candidates with laboratory activity against disease-causing bacteria, simply by searching this space computationally, points to a different approach. “We ran ApexGO for a few months and found hundreds of candidates,” notes Gardner. “If we ran that process for a year, how many thousands of these could we find?”

“This result points toward a future in which we can optimize molecules for a desired function in a fraction of the time,” adds de la Fuente, “using machines to guide discovery through chemical spaces too vast for humans to explore by trial and error.”

Future Directions

While some of the molecules proposed by ApexGO showed promising antibiotic activity, the researchers emphasize that even the best-performing peptides are still early-stage candidates. Before any could be used to treat infections in humans, they would need to be further optimized for safety, stability and how long they remain active in the body.

Still, the study suggests that AI can help researchers decide which molecules are worth making and testing in the first place. Instead of synthesizing one candidate after another by trial and error, tools like ApexGO could help narrow the search to molecules more likely to work.

For de la Fuente, that approach could eventually extend beyond antibiotics. “In this case, we wanted to optimize peptides for antimicrobial activity,” he says. “But you could imagine applying the same idea to peptides with other biological functions, like modulating the immune system or targeting tumors.” Gardner’s lab is already exploring related approaches using AI agents, which may be able to draw on scientific knowledge and reason through design choices. 

“The larger idea is that AI can help scientists search spaces that are too large to explore by hand,” says Gardner. “ApexGO is one example of that. The next generation of tools may be able to explore these spaces in even more flexible ways.”

“ApexGO shows that AI can do more than predict which molecules might work: it can help us improve them,” adds de la Fuente. “At a time when antibiotic resistance is rising worldwide, we need technologies that help us move faster from an idea to a real therapeutic candidate. ApexGO is an important step toward that future.”

This study was conducted at the University of Pennsylvania and supported by the National Institutes of Health (R35GM138201), the Defense Threat Reduction Agency (HDTRA1-21-1-0014), the National Science Foundation (IIS-2145644, DBI-2400135) and a National Science Foundation graduate research fellowship.

Additional co-authors include co-first author Fangping Wang of the Perelman School of Medicine, School of Engineering and Applied Science, and School of Arts & Sciences; and Natalie Maus of the School of Engineering and Applied Science.