Artificial intelligence (AI) has been increasingly applied in healthcare, supporting functions such as medical imaging, clinical documentation, personalized medicine, and predictive analytics. Such AI applications have also yielded promising results in drug discovery and development, with the AstraZeneca CEO recently saying that artificial intelligence is helping the company develop medicines faster, identify promising drug targets, and improve the odds of success in costly clinical trials.
Ahmed Louri, David and Marilyn Karlgaard Endowed Chair Professor of Electrical and Computer Engineering and IEEE Life Fellow, was recently awarded a $1 million National Science Foundation (NSF) grant to support his work using AI and computer architecture to accelerate and improve the efficiency of drug discovery.
“I’ve worked in computer architecture and high-performance computing for a long time. Recently, I wanted to branch into healthcare and help humanity in that regard,” Prof. Louri began. “I got interested in seeing if we can apply some of the advances in AI and computer architecture to see if we can speed up the biological and life sciences applications,” he said.
Louri collaborated with a former student, Assistant Professor Hao Zheng of the Department of Electrical and Computer Engineering at the University of Central Florida (UCF), and Wei Zheng from the life sciences department at UCF to discuss how they could apply graph neural networks (GNNs) to drug discovery.
The four-year project will start this summer and yield new AI algorithms and hardware for scalable, efficient analysis of complex biological networks. Ideally, the results will enable faster and more accurate modeling of molecular and genomic interactions, leading to new insights into how our genes and other biological systems function.
The team is focused on sharing the data and findings with the community as soon as they can. As Louri puts it, “this is how research snowballs.”
When asked about the project’s future, Louri shared that he and his team aspire to use this project as a launching pad for lasting impact. “We hope to recruit students and train them in this area. We’re considering this a seed-type project, as we plan to submit more large-scale proposals to other funding agencies, such as the NIH, to support this work and drive further innovation in this critical area.”