The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery

  • Authors:

  • Diana M. Negoescu;Peter I. Frazier;Warren B. Powell

  • Affiliations:

  • Department of Management Science and Engineering, Stanford University, Stanford, California 94305;School of Operations Research and Information Engineering, Cornell University, Ithaca, New York 14853;Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08544

  • Venue:
  • INFORMS Journal on Computing
  • Year:
  • 2011

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Abstract

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 the molecule that best treats a given disease. The problem of choosing 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, that uses correlations in our Bayesian prior distribution between the performance of different alternatives (molecules) to dramatically reduce the number of molecular tests required, but it has heavy computational requirements that limit the number of possible alternatives to a few thousand. We develop computational improvements that allow the knowledge-gradient method to consider much larger sets of alternatives, and we demonstrate the method on a problem with 87,120 alternatives.