Researchers at the University of Chicago Medicine Comprehensive Cancer Center are embarking on a new project to address drug-resistant tumors using artificial intelligence and high-performance computing from Argonne National Laboratory. The initiative, supported by $6 million in funding as part of a larger $15 million project, aims to accelerate the discovery of cancer therapies through advanced AI and machine learning techniques.
The funding is provided by the Advanced Research Projects Agency for Health (ARPA-H), an agency under the U.S. Department of Health and Human Services established in 2022. The goal is to speed up transformative biomedical research efforts.
"The drug discovery process is long, inefficient and costly, with the majority of new drugs failing during clinical trials," said Kunle Odunsi, who holds multiple roles at the University of Chicago including Director of the Comprehensive Cancer Center. "Patients with cancer don’t have time to wait for new treatments, so there is a strong need to compress the drug discovery timeline."
The joint project, named "Integrated AI and Experimental Approaches for Targeting Intrinsically Disordered Proteins in Designing Anticancer Ligands" (IDEAL), will focus on narrowing down potential compounds that can be developed into effective treatments. This effort will involve researchers from both UChicago Medicine and Argonne National Laboratory.
With Argonne's expertise in AI combined with UChicago's strengths in cancer research, co-investigator Thomas Brettin expressed optimism about addressing complex scientific challenges related to cancer.
Argonne’s facilities like the Aurora exascale supercomputer and Advanced Photon Source will play a crucial role by enabling rapid screening and simulation processes necessary for this research.
The IDEAL team plans to initially test their model on ovarian cancer targets but aims for broader applications across various cancer types. Odunsi believes this collaboration could significantly impact how quickly new treatments reach patients facing poor prognoses.