Group Testing With Blockers and Synergism

Abstract

Discovery and development of a new drug can cost hundreds of millions of dollars. Pharmaceutical companies have used group testing methodology routinely as one of the efficient high throughput screening techniques to search for “lead” compounds among collections of hundreds of thousands of chemical compounds. The lead compounds can be modified to produce new and effective molecules, which eventually may lead to new drugs. This article develops models and estimation procedures to obtain quantitative information from data in such applications. It investigates group testing procedures and studies cost efficiency when the standard assumption adopted by Dorfman, that tested items act independently of one another, is violated. The investigation is focused on, but not limited to, the square array pooling method, and the methodologies developed are illustrated through simulations and a drug discovery dataset from Glaxo Wellcome Inc.