The University of Florida College of Pharmacy is harnessing artificial intelligence to rethink how medicines are discovered, tested and delivered. With a top national ranking and access to HiPerGator 3.0—the fastest AI supercomputer in higher education—the college pairs computational power with clinical expertise to tackle problems ranging from cancer therapeutics to the opioid crisis. By embedding AI in classroom instruction and research activities, UF prepares pharmacists and pharmaceutical scientists to use machine learning and data-driven tools across careers.
Across campus, teams combine clinical insight with algorithmic methods to answer practical questions about medication safety, trial design and individualized therapy. The college also leverages national claims data covering more than 350 million lives to develop population-level models, and supports projects that move from in silico prediction to bench validation and clinical implementation. These efforts are framed by an institutional commitment to ethical, equitable and evidence-based use of AI in health care.
AI across the drug lifecycle
Drug discovery and molecular design
Researchers in medicinal chemistry and related labs use computational approaches to accelerate lead identification and optimization. Teams led by Drs. Chenglong Li and Yanjun Li integrate deep learning with experimental screening to create platforms for automated small-molecule design. Collaborations with Dr. Gustavo Seabra focus on training models that can propose, evaluate and prioritize candidate compounds for synthesis, while Dr. Wenjun Xie combines computational chemistry and biochemical assays to explore enzyme evolution and protein design. These projects frequently deploy small molecule drug design platform concepts and closed-loop cycles that iterate between model prediction and laboratory testing.
Clinical trials, safety and translational research
On the clinical side, AI helps design smarter trials and improve medication safety. Investigators such as Drs. Sarah Kim and Caitrin McDonough apply AI-assisted imaging analysis and multimodal modeling to inform trial endpoints and predict disease progression. Pharmacogenomics-focused teams—led by Drs. Julio Duarte, Yan Gong and Khoa Nguyen—are creating decision support that brings genetic information to prescribers before therapy begins, using preemptive pharmacogenetic testing frameworks. Pharmacoepidemiologists like Drs. Tianze Jiao, Md Mahmudul Hasan and Steven Smith use advanced study designs and machine learning on longitudinal databases to generate affordable, disease-specific precision medicine strategies that are also implementation-ready.
People and infrastructure driving progress
Faculty expertise and flagship projects
The college’s momentum rests on a broad roster of investigators applying AI in diverse ways. Dr. Jatinder Lamba leads work in leukemia genomics and created the Acute Leukemia Methylome Atlas (ALMA), a resource reported in the Journal of Biomedical Informatics. Dr. Brandon Warren uses neural activity modeling to study substance use behaviors, while Dr. Masoud Rouhizadeh develops natural language processing tools to extract clinical signals from unstructured records. Other contributors—such as Dr. Almut Winterstein on drug safety in maternal and pediatric populations—use AI to build prediction models and improve variable measurement in large healthcare databases.
Facilities, funding and collaborative hubs
Physical and computational assets amplify research impact. The new Malachowsky Hall for Data Science & Information Technology—opened fall 2026—brings the Colleges of Pharmacy, Medicine and Engineering into a single interdisciplinary setting, with more than 263,000 square feet dedicated to data-driven discovery. Coupled with HiPerGator 3.0 and access to vast national claims resources, the environment enables projects that span from algorithm development to clinical deployment. A recently announced centralized AI hub will further coordinate the college’s diverse initiatives, and targeted grants—such as Department of Defense funding for enzyme engineering—are expanding experimental and computational capabilities.
Funding and partnerships
National agencies and foundations support many of these AI programs. Sponsors include the National Institute of Allergy and Infectious Diseases, the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institute on Drug Abuse, the National Institute on Mental Health, and the National Heart, Lung, and Blood Institute, along with organizations such as the Juvenile Diabetes Research Foundation and the PhRMA Foundation. This portfolio enables translational work that moves models from retrospective analysis to prospective testing and policy-relevant solutions.
Looking ahead
As demand grows for professionals who can blend clinical knowledge with data science, the college is recruiting faculty who will lead AI-driven advances and train the next generation of practitioners. From algorithmic drug design to clinical decision support embedded in electronic medical records, the University of Florida College of Pharmacy is building an ecosystem where artificial intelligence supports safer prescribing, more efficient trials and more personalized care. Those interested in collaborating or learning more will find an active, well-resourced community advancing the responsible use of AI in health.
