The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
The Knowledge-Gradient Algorithm for Sequencing Experiments in Drug Discovery
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DOI：
10.1287/ijoc.1100.0417
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发表时间：
2011-07
期刊：
影响因子：
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通讯作者：
Diana M. Negoescu;P. Frazier;Warrren B Powell
中科院分区：
文献类型：
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作者：
Diana M. Negoescu;P. Frazier;Warrren B Powell
我们提出了一种在药物发现中自适应地选择要测试的分子化合物序列的新技术。从碱基化合物开始，我们考虑寻找最能治疗特定疾病的分子的化学衍生物的问题。选择要测试的分子以最大限度地提高所发现的最佳化合物的预期质量的问题，可以在数学上表述为一个排序和选择问题，在这个问题中，每个分子都是替代的。我们应用了一种最近开发的算法，称为知识梯度算法，该算法在我们的贝叶斯先验分布中使用不同替代品(分子)性能之间的相关性，以显著减少所需的分子测试数量，但它具有繁重的计算要求，将可能的替代方案的数量限制在数千个。我们开发了计算改进，使知识梯度方法可以考虑更大的备选方案集，并在87,120个备选方案的问题上演示了该方法。
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.