Beroza P, Bradley E K, Eksterowicz J E, Feinstein R, Greene J, Grootenhuis P D, Henne R M, Mount J, Shirley W A, Smellie A, Stanton R V, Spellmeyer D C
DuPont Pharmaceuticals Research Laboratories, 150 California Street, San Francisco, CA 94111, USA.
J Mol Graph Model. 2000 Aug-Oct;18(4-5):335-42.
We describe statistical techniques for effective evaluation of large virtual combinatorial libraries (> 10(10) potential compounds). The methods described are used for computationally evaluating templates (prioritization of candidate libraries for synthesis and screening) and for the design of individual combinatorial libraries (e.g., for a given diversity site, reagents can be selected based on the estimated frequency with which they appear in products that pass a computational filter). These statistical methods are powerful because they provide a simple way to estimate the properties of the overall library without explicitly enumerating all of the possible products. In addition, they are fast and simple, and the amount of sampling required to achieve a desired precision is calculable. In this article, we discuss the computational methods that allow random product selection from a combinatorial library and the statistics involved in estimating errors from quantities obtained from such samples. We then describe three examples: (1) an estimate of average molecular weight for the several billion possible products in a four-component Ugi reaction, a quantity that can be calculated exactly for comparison; (2) the prioritization of four templates for combinatorial synthesis using a computational filter based on four-point pharmacophores; and (3) selection of reagents for the four-component Ugi reaction based on their frequency of occurrence in products that pass a pharmacophore filter.
我们描述了用于有效评估大型虚拟组合文库（>10¹⁰种潜在化合物）的统计技术。所描述的方法用于通过计算评估模板（对用于合成和筛选的候选文库进行优先级排序）以及设计单个组合文库（例如，对于给定的多样性位点，可以根据它们在通过计算筛选的产物中出现的估计频率来选择试剂）。这些统计方法很强大，因为它们提供了一种简单的方法来估计整个文库的性质，而无需明确列举所有可能的产物。此外，它们快速且简单，并且实现所需精度所需的采样量是可计算的。在本文中，我们讨论了允许从组合文库中随机选择产物的计算方法以及从此类样本获得的数量估计误差所涉及的统计数据。然后我们描述三个例子：（1）对四组分乌吉反应中数十亿种可能产物的平均分子量进行估计，这一数量可以精确计算以作比较；（2）使用基于四点药效团的计算筛选对用于组合合成的四个模板进行优先级排序；（3）根据试剂在通过药效团筛选的产物中的出现频率选择用于四组分乌吉反应的试剂。
