SPEAKER: Dr. Abhyuday Mandal
Department of Statistics
University of Georgia

ABSTRACT:

Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in pharmaceutical industry. An efficient solution to this problem will help reduce the expenditure at the early stages of drug discovery. In an attempt to solve this problem, Mandal, Wu and Johnson (2006) proposed the SELC algorithm. Although powerful, it fails to extract substantial information from the data to guide the search efficiently as this methodology is not based on any statistical modeling. The proposed approach uses Gaussian Process modeling to improve upon SELC, and hence named G-SELC. The performance of the proposed methodology is illustrated using four and five dimensional test functions. Finally, we implement the new algorithm on a real pharmaceutical data set for finding a group of chemical compounds with optimal properties.

(Joint research with C.F. Jeff Wu and Pritam Ranjan)