"G-SELC: A New Global Optimization Technique Using Genetic Algorithms, Tabu Search and Gaussian Processes"

University Of Georgia Research Foundation Inc, Athens GA

Investigators

Abstract

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5). In this work, the investigator develops a new global optimization technique, which is primarily motivated by applications in drug discovery. Although identification of useful compounds is critical to improving efficiency in drug discovery, pharmaceutical industries generally adopt ad hoc approaches to identify promising compounds. The proposed research aims to develop an efficient technique named G-SELC, which expedites this process. To this end, the investigator develops a global optimization procedure using local search techniques such as Genetic Algorithms and Tabu Search combined with statistical modeling involving Gaussian processes. This research is also extended to categorical variables in Gaussian process. In addition, the investigator develops efficient numerical techniques to reduce computational burden for this batch sequential optimization problem. Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in pharmaceutical industry. The proposed research helps reduce the expenditure at the early stages of drug discovery, thus creating significant economic and social benefits. This optimization technique is also used to identify optimal solutions in many other scientific research problems such as computer experiments, functional magnetic resonance imaging and nanotechnology. The investigator shows that in some applications, categorical variables can be treated as continuous. This simplifies the computation in Gaussian process modeling significantly. It has far-reaching consequences not only in drug discovery, but also in complex computer modeling where Gaussian process modeling is used extensively which includes modeling air quality, calibration of computational models of cerebral blood flow, predicting climate and weather, statistical mechanics of granular flow, terrestrial models, dynamics of infectious diseases and so on.