Optimal Learning for Drug Discovery in Ewing's Sarcoma

We present a new technique for adaptively choosing the sequence of molecular compounds to test in drug discovery. Beginning with a base compound, we consider the problem of searching for a chemical derivative of this molecule that best treats a given disease. The problem of choosing the molecules to test to maximize the expected quality of the best compound discovered may be formulated mathematically as a ranking and selection problem, in which each molecule is an alternative. We apply a recently developed algorithm, known as the knowledge-gradient algorithm. This algorithm uses correlations in our Bayesian prior belief between the performance of different alternatives (molecules) to dramatically reduce the number of molecular tests required, but has heavy computational requirements that limit the number of alternatives that can be considered to a few thousand. We develop computational improvements that allow the knowledge-gradient method to consider much larger sets of alternatives, and demonstrate the method on a problem with 87120 alternatives.