ARTIFICIAL INTELLIGENCE Empowering the future of pharmacy

Artificial intelligence is more than just the next wave of high-tech. It is transforming nearly every sector of our life and the economy, including health care. In the UF College of Pharmacy, researchers are using AI tools to address the nation’s health care challenges from developing new cancer drugs to stemming the opioid epidemic. AI is more than a big idea — it’s changing the way we think about pharmacy and allowing us to answer big questions to improve health outcomes and patient care.

building an ai-powered future

UNIVERSITY OF FLORIDA

AI's Far-Reaching Impact

Anchored by a $70 million research and education partnership with NVIDIA, UF is building the most powerful supercomputer in higher education and has committed to hiring 100 new faculty dedicated to AI. UF’s powerful AI infrastructure will put researchers on the front lines of addressing some of the world’s most pressing challenges.

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AI Experts IN THE UF COLLEGE OF PHARMACY

MEDICINAL CHEMISTRY

Chenglong Li, Ph.D.

Li is using computing AI approaches combined with lab experiments to advance the drug discovery process. AI technology is assisting his lab in building 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, lung, colon and breast cancer.

Chenglong Li

MEDICINAL CHEMISTRY

Gustavo Seabra, Ph.D.

Seabra is developing, in collaboration with Dr. Chenglong Li, applications that teach AIs to autonomously design, screen and optimize molecules that attack selected disease targets. The process can speed up the drug discovery process by suggesting molecules to be synthesized and tested with improved chances for success.

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Pharmacodynamics

Brandon Warren, Ph.D.

Warren intends to use AI algorithms to analyze network activity of calcium dynamics in deep brain structures and correlate the activity of individual neurons with behavioral measures. In particular, he’s interested in cell patterns that are activated during cocaine-seeking versus food-seeking behaviors.

Brandon Warren

Pharmaceutics

Sarah Kim, Ph.D.

Kim envisions developing AI-assisted imaging analysis and informatics tools that will accelerate the analysis of imaging data and provide insights into better understanding of disease progression. Ultimately, the AI-based tools will help in the rational design of disease prevention and treatment strategies. She is currently focusing on optimizing clinical trial designs for Duchenne muscular dystrophy and type 1 diabetes with other UF investigators.

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Pharmacotherapy and translational research

Julio Duarte, Pharm.D., Ph.D.

Duarte is using AI to develop novel methods to identify patients most likely to benefit from preemptive pharmacogenetic testing. Effective targeting of preemptive testing should improve personalization of medication prescribing by assuring genetic information is available to prescribers before a prescription is written.

Julio Duarte

Pharmacotherapy and translational research

Yan Gong, Ph.D.

Gong collaborates with multiple UF faculty in applying AI tools to her pharmacogenomics and precision medicine research. AI technology is assisting her research team in improving the model performance of multiple data sets that are used to advance the clinical implementation of precision medicine.

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PHARMACOTHERAPY AND TRANSLATIONAL RESEARCH

Caitrin McDonough, Ph.D.

McDonough is adopting new AI strategies into her data analysis to characterize and predict cardiovascular disease and cardiovascular drug response. Machine learning and AI tools are helping her integrate data from multiple sources and go beyond traditional statistical models in predicting cardiovascular events.

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PHARMACOTHERAPY AND TRANSLATIONAL RESEARCH

Khoa Nguyen, Pharm.D.

With expertise in pharmacy informatics and pharmacogenomics, Nguyen helps develop clinical decision support tools for UF Health’s electronic medical record system. He envisions using AI technology to develop and implement user-centered health information technology into the medical record system, which will help predict and prevent severe adverse drug reactions in patients.

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PHARMACOTHERAPY AND TRANSLATIONAL RESEARCH

Steven Smith, Pharm.D., M.P.H.

Smith uses prediction modeling, including machine learning techniques, to develop improved methods for selecting antihypertensive treatments in patients with chronically high blood pressure. He is also applying similar techniques to improve the value of electronic health record databases in medical research by developing models that differentiate between patients with high and low degrees of data missingness.

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Pharmaceutical outcomes and policy

Jingchuan “Serena” Guo, M.D., Ph.D.

By using large health care databases and machine learning technology, Guo is studying the delivery of high-quality health care, the safety and effectiveness of therapies and the accurate prediction of health outcomes. She is also evaluating bias and fairness of AI applications and exploring new approaches to mitigate the observed bias.

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PHARMACEUTICAL OUTCOMES AND POLICY

Weihsuan “Jenny” Lo-Ciganic, Ph.D., M.S., M.S. Pharm.

Lo-Ciganic’s research uses innovative AI approaches to addresses critical public health problems by developing, refining and validating prediction algorithms that will identify individuals most at risk of unsafe medication use and adverse outcomes. She is one of a select group of pharmacoepidemiologists in the United States to incorporate pharmacoepidemiology and innovative data science research aimed at addressing the nation’s opioid epidemic.

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Pharmaceutical Outcomes and Policy

Hui Shao, M.D., Ph.D.

Shao has focused his AI-related research on developing microsimulation models to predict the progression of diseases and using machine-learning techniques to identify individualized treatment responses and modulators for treatment effect. He has developed a series of risk engines/equations/decision tools to support individualized therapeutic planning in clinical practice. He has also applied his microsimulation modeling techniques in policy evaluation.

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Pharmaceutical Outcomes and Policy

Almut Winterstein, R.Ph., Ph.D.

Winterstein’s research concentrates on the evaluation of drug safety and effectiveness in real-world populations with a specific focus on child and maternal health and pediatrics. She employs AI methods to optimize measurement of variables in large health care databases and to develop prediction models for adverse drug effects. Examples of recent work include machine learning-based imputation methods to predict pregnancy onset for the evaluation of teratogenic drug effects and prediction of drug-induced hypoglycemia in hospitalized patients.

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AI Research across the entire life cycle of a drug

AI wheel

Endless opportunities in AI

Artificial intelligence is enabling faculty across the UF College of Pharmacy’s five departments to accelerate and advance research and clinical care in the pharmaceutical sciences and practice. AI is transforming the way we approach drug discovery, design clinical trials, recommend more personalized treatments and make medications safer. With support from HiPerGator 3.0 — the fastest AI supercomputer in higher education — and national claims data for more than 350 million lives available at the UF College of Pharmacy, we are well equipped with the tools and technology to explore endless opportunities in advancing AI research and practice.

Funding Agencies

Multiple national funding agencies are supporting AI-related research in the UF College of Pharmacy, including :

  • National Institute on Aging
  • National Institute on Drug Abuse
  • National Institute on Mental Health
  • National Heart, Lung, and Blood Institute
  • National Cancer Institute
  • Juvenile Diabetes Research Foundation
  • Richard King Mellon Foundation
AI in health care

Advancing AI in the health sciences

Artificial Intelligence Faculty Job Openings

The academic colleges of UF Health are recruiting faculty who can harness artificial intelligence to improve health and health equity, while training students to lead tomorrow's AI workforce.

Malachowsky hall for Data science & information technology

OPENING Spring 2023

Malachowsky Hall for Data Science & Information Technology

In the spring of 2023, artificial intelligence and data science researchers from the UF Colleges of Pharmacy, Medicine and Engineering will move into the new state-of-the-art Malachowsky Hall for Data Science & Information Technology. The 263,000-square-foot building will be located in the heart of UF's main campus and connect students and researchers from across disciplines by creating an interdisciplinary hub for advances in computing, communication and cyber-technologies.

Data Science Building

Location

Malachowsky Hall for Data Science & Information Technology will be located in the heart of UF’s Gainesville campus across the street from the J. Wayne Reitz Union.

Malachowsky Hall for Data Science & Information Technology

Collaboration

The building’s design will promote collaboration amongst faculty, staff and students from the Colleges of Pharmacy, Medicine and Engineering.

Data Science Building

State-of-the-Art

With more than 260,000 square feet, Malachowsky Hall for Data Science & Information Technology will set a new standard for data science buildings across the country.

Data Science Building

Pharmacy’s New Home

The College of Pharmacy’s department of pharmaceutical outcomes and policy, Center for Drug Evaluation and Safety and the Consortium for Medical Marijuana Clinical Outcomes Research will occupy the sixth floor, along with other computational researchers in the college.

Malachowsky Hall for Data Science & Information Technology