Wei-Hsuan “Jenny” Lo-Ciganic’s introduction to artificial intelligence came in 2009. As a doctoral student at the University of Pittsburgh, she pursued a master’s degree in biostatistics, where she learned about tree-based machine learning methods.

“At the time, I was like ‘why am I learning this? Am I going to use this in the future?’” said Lo-Ciganic, Ph.D., M.S., M.S.Pharm., an associate professor of pharmaceutical outcomes and policy in the UF College of Pharmacy. “Now when I look back, I am thankful. It put me on the top of the curve. I’m a pioneer in our field using machine learning approaches.”

Machine learning is a subset of AI. Whereas AI features techniques that enable computers to mimic human behavior, machine learning uses computer systems that learn and adapt without explicit instructions. Machine learning allows scientists to discover hidden patterns and incorporate complex interactions in large data to generate more accurate predictions in clinical settings.

Lo-Ciganic applies machine learning in three areas of research — identifying individuals at high risk of substance abuse disorders, improving medication adherence and predicting treatment failures. She has secured federal funding from the National Institute on Drug Abuse and the National Institute on Aging and successfully developed machine learning algorithms using health care claims data to predict patients who are at high risk for opioid overdose and opioid use disorder. The models have outperformed traditional statistical approaches, which tend to target people who are not truly at risk and miss the majority of individuals who are at risk.

“If we can more effectively predict who poses the greatest risk of an opioid overdose, then we can help clinicians allocate their time and resources to patients who need intervention the most — rather than targeting an entire population,” Lo-Ciganic said.

Incorporating machine learning into clinical decision-making is an idea also being studied by Caitrin McDonough, Ph.D., M.S., an assistant professor of pharmacotherapy and translational research. She is adopting new AI strategies into her data analysis to predict patients at high risk for cardiovascular disease. Machine learning and AI tools are helping her go beyond traditional statistical models in predicting cardiovascular events.

“With traditional models, I’m often using my own expertise and prior literature to determine factors for analysis,” McDonough said. “AI and machine learning have the computational power to build really robust models, and include additional factors, which could make a significant impact on pharmacotherapy.”

McDonough’s long-term goal is to build models that run continuously over data living within the electronic health records and flag high-risk hypertension patients. Providing this clinical decision support to physicians and caregivers has immense potential, especially in the area of adherence. She said machine learning technology could help layer electronic health record and insurance claims data to understand why patients are not filling their prescriptions or taking blood pressure medications.

“I was drawn to study cardiovascular disease by the sheer number of people impacted by this health condition,” McDonough said. “Our blood pressure control rates across this country are not great, but if we can provide patients the right medication and get them to take their medication, it could prevent a lot of other complications. The AI-supported modeling I’m developing around cardiovascular disease is a step forward toward improving patient care.”

While Lo-Ciganic and McDonough have their sights set on using AI to improve health outcomes and patient care, Chenglong Li, Ph.D., a professor of medicinal chemistry, has embraced an AI-based approach for drug discovery. His lab allocates a lot of energy into optimizing a compound to become a drug. It’s easy to find an initial hit compound, but it’s hard to turn the compound into a drug.

AI is assisting Li’s lab in building neural network models to predict the binding of small molecules to their disease targets and in developing a computational small molecule drug design platform to optimize newly developed hit compounds. These compounds could one day lead to new drug therapies to treat pancreatic, liver, prostate or breast cancers.

“We have the potential to generate a more efficient molecule using AI,” Li said. “For example, if we have a lead compound, and we don’t know how to make a better one, the traditional way is to rely on organic chemistry instinct and make a lot of similar compounds. We might need to make 100 compounds to design a better one, but with our newly designed platform supported by AI, we can select the top five compounds to synthesize and make a better compound. That’s enhanced efficiency and improved productivity.”

Li, who serves as the Nicholas Bodor Professor in Drug Discovery in the UF College of Pharmacy, began incorporating AI into his lab in 2018. He acknowledges that the structure-based drug design field is limited right now by the amount of high-quality data available, but he credits AI for improving drug optimization.

“In the traditional way of doing drug design, you are basically shooting things in the dark and hoping to capture something. There’s a lot of serendipity,” Li said. “If we can take computing AI approaches and combine with lab experiments, we can really expand the drug optimization options and find new drugs more effectively and efficiently.”

As the sun rises on another picturesque Florida morning, beams of light reflect off the facade of the Malachowsky Hall for Data Science & Information Technology — UF’s new hub for artificial intelligence. An expansive wall of windows is geometrically positioned, so as the sun traverses across the sky, the light of day creates an illusion that the building is in motion. Inside, the morning glow fills open spaces with natural light, as the first faculty and staff arrive for work. UF students are already occupying the three-story atrium, as informal conversations grow louder. This is the collision point for a multidisciplinary group of scientists and students focused on data science, artificial intelligence and information technology.

The Malachowsky Hall for Data Science & Information Technology won’t open until 2023, but it’s never too early to imagine how this building will transform data science research and collaboration on campus. AI and data science researchers from the UF colleges of Pharmacy, Medicine and Engineering, along with the Informatics Institute, will work side by side in expanding UF’s AI research interests, while training thousands of students in AI each year.

The UF College of Pharmacy will have a significant footprint in the 263,000-square-foot building, with faculty from the department of pharmaceutical outcomes and policy, and other data scientists, the Center for Drug Evaluation and Safety, or CoDES, and the Consortium for Medical Marijuana Clinical Outcomes Research occupying the sixth floor.

“The idea that we’re moving in with colleagues in the data science space is very exciting,” said Almut Winterstein, R.Ph., Ph.D., FISPE, a professor and the Robert and Barbara Crisafi Chair in Pharmaceutical Outcomes and Policy in the College of Pharmacy and director of CoDES at UF. “While remaining grounded in health care and pharmacy, our faculty will have countless opportunities for exchange with other data scientists. Being closer to colleagues in the colleges of Medicine and Engineering will help with new collaborations and innovative research approaches that we may have never imagined.”

The Malachowsky Hall for Data Science & Information Technology will be located in the heart of UF’s campus, across Museum Drive from the UF Welcome Center and J. Wayne Reitz Union. UF broke ground in December on the $150 million building. An array of donors have stepped forward to support the project, including $500,000 gifts from the DuBow Family Foundation in Jacksonville and Carl, ’76, and Joan Allison in Lake City — long-time supporters of the UF College of Pharmacy.

“We’re grateful for the DuBow and Allison families, whose philanthropic support over the years has propelled
the UF College of Pharmacy to become one of the nation’s top 5-ranked pharmacy colleges,” Johnson said. “Malachowsky Hall for Data Science & Information Technology will be a beautiful and visually interesting addition to campus, but more importantly, it will be a space where innovation and data-driven solutions are realized to improve health care.”

A transformative $750,000 gift from University of Florida College of Pharmacy alumna and long-time donor Debbie DeSantis will further expand the role artificial intelligence plays in precision medicine research. AI-powered precision medicine is capitalizing on high-performance computing capabilities to provide researchers and clinicians the tools and information necessary to tailor interventions around an individual’s health. By leveraging AI technology, faculty specializing in precision medicine are using big data to better predict cardiovascular disease, evaluate acute myeloid leukemia prognosis and identify new ways to implement preventive pharmacogenomics testing.

DeSantis’ gift will establish the first endowed term professorship in precision medicine in the UF College of Pharmacy. Endowed professorships are among the most significant awards conferred to a UF faculty member and this support will enable the college to recruit or retain a preeminent researcher. Additional funding will help the UF College of Pharmacy create a mechanism to support students and trainees within precision medicine — paving the way for future pharmacists and pharmaceutical scientists to jump-start innovative new projects.

“Debbie’s gift will make a significant difference for the faculty, students and trainees who will benefit directly from her philanthropic support, but also the lives touched by the groundbreaking discoveries that will come out of our program,” said Julie Johnson, Pharm.D., dean and distinguished professor of the UF College of Pharmacy. “The intersection of AI and precision medicine will lead to new ideas and approaches in health care, and that excites all of us in the College of Pharmacy who are committed to improving the health of the patients we serve.”

DeSantis graduated with honors from the UF College of Pharmacy with a bachelor’s degree in 1982. For more than two decades, she has generously supported her alma mater and helped the college achieve a top 5 national ranking.