PHILADELPHIA (October 30, 2018) – John Karanicolas, PhD, an associate professor at Fox Chase Cancer Center, has received a grant from the National Science Foundation to conduct deep learning to design selective kinase-targeted chemical probes.
Karanicolas joined Fox Chase Cancer Center in August 2016 from the University of Kansas and has a national reputation in the field of computational chemistry, an area that focuses on mathematical modelling in research.
The human genome encodes over 500 protein kinases, many of which play key roles in processes dis-regulated in cancer. Already more than 30 protein kinase inhibitors have been approved as drugs for treatment of specific cancers. However, the strong similarity between kinases presents an important problem: inhibitors intended to target a single kinase can also inhibit other “off-target” kinases. To address this, the goal of this grant is to train, test, and use a deep learning model for predicting kinase inhibitor selectivity. The project is entitled “D3SC: EAGER: Deep Learning to Design Selective Kinase Inhibitors.”
The award is for $298,559 and runs from September 1, 2018, to August 31, 2020.