Artificial intelligence plays a crucial role in the groundbreaking neuroscience research taking place in the Lieber Institute for Brain Development’s laboratories. But exactly how will deep learning or AI accelerate our scientists’ quest to bring new treatments and diagnostic tools to patients and their families?
Lieber Institute Chief Advancement Officer Geoff DeLizzio recently sat down with two of the Lieber Institute’s AI leaders to learn more. In a free, live webinar last month, Lead Investigator Shizhong Han, Ph.D., and Staff Scientist Michael Nagle, Ph.D., shared their experiences with AI so far and their hopes and concerns for the science moving forward.
The Lieber Institute’s structure presents a unique model in which researchers can employ AI. As an independent nonprofit biomedical research firm located on the Johns Hopkins medical campus in Baltimore, LIBD allows researchers to explore new questions in science rather than focusing their time on generating grant funding and further examining already-proven concepts.
“We have the freedom to pursue innovative ideas without the constant pressure to get grants,” Dr. Han said.
That freedom has allowed Lieber Institute scientists to use AI to accelerate their efforts to identify new drug targets to treat brain diseases, screen existing chemicals for their ability to address a given drug target, and even create entirely new drugs for conditions such as schizophrenia, mood disorders, anxiety, Parkinson’s disease and more.
In fact, Dr. Han, Dr. Nagle and Staff Scientist Jiyun Zhou, Ph.D., were chosen as winners of the 2024 Amazon Web Services (AWS) IMAGINE Grant, a public grant opportunity open to nonprofits that are using technology to solve the world’s most pressing challenges. The Lieber Institute was named a winner in the Pathfinder – Generative AI category which recognizes highly innovative, mission-critical projects that leverage generation AI. The Lieber Institute will receive up to $200,000 in unrestricted funding, up to $100,000 in AWS Promotional Credits, and implementation support from the AWS Generative AI Innovation Center.
The team is using the funding to create a new AI tool, called Generative Reinforcement Alignment of Predicted Expression, or GRAPE, that will design new molecular structures for potential drugs based on the known gene expression patterns of conditions like schizophrenia.
GRAPE is unique in that it will use generative AI to design new drugs—an approach proven in previous research—combined with predictive AI to evaluate the effectiveness of the new drugs.
“GRAPE will bridge our gene expression datasets and our drug development capabilities, bringing new value to our data resources and directly supporting our mission to translate biological insights into improved treatments for people living with schizophrenia and other serious psychiatric disorders,” says Dr. Han.
Watch our free AI webinar to learn more about AI@LIBD.