The Knowledge‐Gradient Algorithm for Sequencing Experiments in Drug Discovery

The Knowledge‐Gradient Algorithm for Sequencing Experiments in Drug Discovery

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Article ID: iaor20117841
Volume: 23
Issue: 3
Start Page Number: 346
End Page Number: 363
Publication Date: Jun 2011
Journal: INFORMS Journal on Computing
Authors: , ,
Keywords: drugs, pharmaceutical industry
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.

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