UToledo’s Northwest Ohio Cancer Research Institute Aims to Speed Up Cancer Breakthroughs

UToledo's Northwest Ohio Cancer Research Institute Aims to Speed Up Cancer Breakthroughs

When researchers at The University of Toledo discovered that a widely used heartburn medication could help make cancer drugs work for triple-negative breast cancer (TNBC) patients, the finding came the slow way: years of lab work, guided in part by computational modeling rather than artificial intelligence (AI).

Now, a new AI-Driven and Structure-Based Drug Discovery Core, within UToledo's newly launched Northwest Ohio Cancer Research Institute, is working to compress that kind of timeline, helping UToledo scientists identify promising drug candidates faster and with greater precision than traditional methods allow.

"By using a mix of lab techniques, computer modeling and AI tools, we are speeding up the development of new cancer treatments," said Dr. JT Zhang, a professor in UToledo's College of Medicine and Life Sciences and founding director of the Northwest Ohio Cancer Research Institute. "The goal is to transform basic discoveries into clinical research and patient care. This core helps to establish the substantial body of evidence required to demonstrate the drug has a plausible chance of working in humans."

Spearheading cancer research in Northwest Ohio

The AI-Driven and Structure-Based Drug Discovery Core is one of four service cores within the Northwest Ohio Cancer Research Institute, which was inaugurated in early 2026 and aims to advance the detection, diagnosis and treatment of cancer by facilitating interdisciplinary collaboration among clinicians at University of Toledo Medical Center and academic researchers at UToledo, as well as partners like ProMedica.

The institute supports University of Toledo President James Holloway's strategic priorities or "launchpads" focusing on experience-based learning, innovation and healthcare.

"The Northwest Ohio Cancer Research Institute is a unique opportunity to advance all of these launchpads as we drive medical research and positively impact the health and well-being of our region," Holloway said. "The institute is positioning us as a regional leader in translational cancer research, elevating the visibility and impact of the innovative work of our faculty and partners while establishing new opportunities to train the next generation of cancer researchers and physician-scientists."

More than 60 experts are founding members, including more than 50 UToledo faculty whose active cancer research is supported by nearly $13 million in grants from federal agencies like the National Institutes of Health.

Northwest Ohio Cancer Research Institute members are collaborating through working groups established to specialize in breast, pancreatic and prostate cancer. They also benefit from shared resources including three service cores in addition to the AI-Driven and Structure-Based Drug Discovery Core: Biorepository, Medicinal Chemistry and In-Vivo Therapeutics.

Two tools working in a loop

The AI-Driven and Structure-Based Drug Discovery Core operates on two connected fronts according to Dr. Jasmine Liu, who directs the operation. One side uses AI to predict which molecules are most likely to bind to and affect a target protein. The other uses structural biology to physically confirm those predictions, determining, in high resolution, exactly how a candidate molecule interacts with its target.

"Those two functions feed each other in a continuous loop," Liu said. "AI predictions help us quickly narrow down and prioritize which molecules are worth testing. Then those compounds can be validated by experiments. The experimental results are then fed back to AI models to improve the next round of predictions for optimization, and it becomes an iterative loop."

Building on a breast cancer breakthrough

The Core is already working with faculty across campus, including the lab of Dr. Zhang, whose team, including Liu, discovered that fatty acid synthase (FASN), an enzyme cancer cells depend on to survive, plays a role in resistance to PARP inhibitors, a class of drugs that helps repair damaged DNA and is used to treat breast, ovarian and prostate cancers.

With the support of funding from the National Cancer Institute, the team found in a 2025 study published in Genes & Diseases that over-the-counter proton pump inhibitors, such as lansoprazole and omeprazole, marketed for acid reflux/heartburn, could block FASN and restore the effectiveness of PARP inhibitors, even in tumors lacking the BRCA gene mutations that the drugs typically require to work.

That initial discovery relied on computational docking, a method scientists used to predict how molecules fit together.

It predates the AI-driven tools now available through the Core. Looking ahead, the team plans to apply AI modeling to that same research, testing the hypothesis that the compound may bind not just to one but multiple sites on the FASN protein, a much larger and more complex target than initially understood.

AI speed with human guardrails

Liu said the AI tools in her service core can generate entirely new candidate compounds for researchers to investigate based on patterns in existing data, quickly narrowing a field of possible molecules down to the small number most likely to work.

"If the lab has already collected enough data to show a group of compounds are active, the AI pipeline can generate completely new compounds that have never existed on this planet and predict that these new compounds should be more active than existing ones."

Despite the added speed, Liu is clear that AI doesn't replace the traditional scientific process - it only accelerates it. Researchers still need to verify the predicted interaction before moving forward.

"The AI tools report a confidence score and that's one of the most important things we look at," she said. "But even after AI generates results, human intervention is still needed, because AI predictions are largely based on learned patterns. We, as human experts, still need to examine the AI results to ensure no laws of physics and chemistry are violated, thereby further increasing the success rate. AI is not perfect. It can make mistakes. But compared to previous computation tools, it is a lot faster, more accurate and versatile, which help us get a lot more promising drug candidates."

Resource for the whole University
Beyond individual projects, the Core is designed to help UToledo faculty generate the kind of preliminary data needed to compete for federal research grants and to support the studies required before a discovery can be tested in patients through an Investigational New Drug (IND) application with the U.S. Food and Drug Administration.

The Core's structural biology data can also help faculty secure time at national synchrotron or Cryo-EM facilities used for advanced structural analysis. These structural data will help secure IND approval.

The Core relies on a GPU server, built to handle the computing demands of AI-driven prediction work, housed alongside the structural biology equipment in a pair of labs.

"Artificial intelligence is creating new possibilities for how we approach cancer research and drug discovery," said Dr. Grace Bochenek, UToledo's vice president of research and innovation. "By combining AI with the expertise of our researchers and the Institute's shared research infrastructure, we can evaluate promising ideas more quickly, explore new therapeutic possibilities and accelerate the path from scientific drug discovery to potential patient impact. This is exactly the kind of interdisciplinary, technology-related research capability that will position UToledo at the forefront of innovation in cancer research."

What's next

Zhang's FASN research remains supported by a five-year, $2.6 million grant from the National Cancer Institute, with the goal of building enough evidence to support a future clinical trial testing the drug combination in TNBC patients.

Both Zhang and Liu believe the Core's AI-driven approach can help move that work, and similar projects across campus, forward more quickly, helping to fulfill the Northwest Ohio Cancer Research Institute's mission.

"Cancer is a leading cause of death worldwide and occurs at higher rates than average in our region of Ohio," said Zhang. "We are hard at work harnessing the strengths of UToledo and our partners to advance scientific discovery, improve patient outcomes and reduce the burden of cancer in and beyond Northwest Ohio."

With the infrastructure now in place, UToledo researchers say the harder work of drug discovery is no longer starting from scratch each time. Instead, AI is accelerating the Northwest Ohio Cancer Research Institute's pace as researchers work to turn a promising molecule into a real treatment that reaches patients.