The process to create medicines and treatments for new infectious diseases and autoimmune diseases is slow and expensive, sometimes taking more than a decade and billions of dollars to bring a drug to market. Can AI (artificial intelligence) help speed antibody drug discovery? New university research aims to help accelerate medical response.
A $3.1 million grant from the National Institutes of Health will support work at The University of Texas in Arlington in applying machine learning to design antibodies that bind to viruses and other antigens. Junzhou Huang, Jenkins Garrett Endowed Professor in the Department of Computer Science and Engineering, aims to use AI to automate and improve the early stages of drug development, specifically antibody design.
Predicting the right binding interactions computationally can help:
- Lower risks.
- Reduce cost of drug development.
- Develop treatment faster.
In addition to the federal grant, Huang’s lab recently received a $200,000 award from Johnson & Johnson to further explore AI-based toxicology prediction, another critical step in drug development. Looking to the future, we are going to see the rise of AI in medicine.