Alexander Tropsha uses AI to search for cures
The UNC Eshelman School of Pharmacy professor is part of a multimillion-dollar project to identify new uses for existing drugs.
In 2024, Alexander Tropsha became one of the first researchers at Carolina to receive an Advanced Research Projects Agency for Health grant — federal funding focused on accelerating health outcomes. The project brings together five research groups from across the country to build a tool that uses artificial intelligence to improve drug repurposing.
“It has been estimated that only 22% of known human diseases have at least one approved drug treatment,” Tropsha says. “If this effort is successful, it will dramatically affect finding cures — especially for rare and neglected diseases that Big Pharma, effectively, cannot afford to work on. It will collectively impact over 10% of people with rare diseases and is the most exciting and, potentially, most impactful project of my life.”
Tropsha is the K.H. Lee Distinguished Professor in UNC Eshelman School of Pharmacy. He also holds appointments in the College of Arts and Sciences’ computer science and biomedical engineering departments, the computing institute RENCI and the UNC School for Data Science and Society.
His field is cheminformatics, a name coined in 1998 by Tropsha’s colleague Frank Brown, then an adjunct professor in the pharmacy school. Tropsha and other researchers worldwide are creating reliable, computational models of chemical data that can forecast new uses or new compounds with a desired property — important in drug discovery and development.
An artificial intelligence approach
In addition to cheminformatics, Tropsha is also an expert in AI. In 2018, he and his colleagues published one of the first papers on generative chemical AI, demonstrating that they could artificially create new chemical entities with desired properties.
For one of his current projects, Tropsha is integrating diverse data sets into structured formats called knowledge graphs, which organize data in a way that allows machines to understand and use it.
Tropsha has built on this work in collaboration with RENCI via an open-source knowledge graph called ROBOKOP. Pulling on information from large biomedical databases, ROBOKOP is a roadmap uncovering answers to questions by examining connections between topics like drugs, diseases and genes.
What genes are involved in disease X? What drugs treat those genes? What side effects do those drugs have? ROBOKOP can provide the answers or create new data-supported hypotheses about these connections.
“Knowledge graphs are excellent at bringing together heterogeneous information into a single system so that it can be more easily explored,” says Chris Bizon, director of data science and analytics at RENCI. “Two previously unconnected pieces of information will sometimes produce an ‘aha’ moment or unexpected discovery that wouldn’t be obvious otherwise.”
A team effort
Bizon is Tropsha’s co-principal investigator for the ARPA-H project, and ROBOKOP is a key component. The team intends to build models and research tools to evaluate every possible drug-disease pair for the likelihood that the drug may treat a disease — approximately 2,700 drugs and 18,500 diseases.
“The entire chemical disease matrix is much bigger than what we considered originally,” Tropsha confirms. “And it includes the challenge of discovering new medications as well as repurposing existing medications for both known and new diseases.”
Tropsha and Bizon are focused on the modeling side of this project and are just part of an incredibly large team that includes Carolina geneticist Melissa Haendel, who works with them to accurately code the information in ROBOKOP.
“We need to adopt an end-to-end approach,” Tropsha says. “We produce analytical suggestions for the data, and our methodologists are making assumptions. We need to know what the clinical team is thinking about so we can modify the process to make our predictions more intelligent and acceptable for the medical world. It’s a feedback loop.”
