Yang Li, PhD, assistant professor of medicine and human genetics at the University of Chicago, and Jingxin Wang, PhD, assistant professor of medicinal chemistry at the University of Kansas, will use RNA splicing modulators -- a type of molecule -- combined with deep-learning models to find molecules able to target the "undruggable proteome" of which 70% currently cannot be targeted by a drug.

Wang's laboratory at the University of Kansas will focus on a subset of 100 genes to systematically identify the splicing regulatory sequence using chemical probes. Li's lab at the University of Chicago will use machine learning to analyze and predict which of the 20,000 human genes are likely to respond to drugs that target RNA splicing.

"If we can precisely map where we can target RNA splicing, then researchers can focus on those genes and RNA sequences for drug development," Wang said in a statement. "After systematic investigation, we will have a comprehensive map of splicing regulatory sequences for the whole human genome, and this will be a very valuable resource, not only for us but for the entire research community."