Xia Xiaoyang, Maliski Edward G, Gallant Paul, Rogers David
Amgen, Inc., One Amgen Center Drive, Thousand Oaks, California 91320, USA.
J Med Chem. 2004 Aug 26;47(18):4463-70. doi: 10.1021/jm0303195.
The use of Bayesian statistics to model both general (multifamily) and specific (single-target) kinase inhibitors is investigated. The approach demonstrates an alternative to current computational methods applied to heterogeneous structure/activity data sets. This approach operates rapidly and is readily modifiable as required. A generalized model generated using inhibitor data from multiple kinase classes shows meaningful enrichment for several specific kinase targets. Such an approach can be used to prioritize compounds for screening or to optimally select compounds from third-party data collections. The observed benefit of the approach is finding compounds that are not structurally related to known actives, or novel targets for which there is not enough information to build a specific kinase model. The general kinase model described was built from a basis of mostly tyrosine kinase inhibitors, with some serine/threonine inhibitors; all the test cases used in prediction were also on tyrosine kinase targets. Confirming the applicability of this technique to other kinase families will be determined once those biological assays become available.
研究了使用贝叶斯统计对通用（多靶点）和特定（单靶点）激酶抑制剂进行建模。该方法展示了一种替代当前应用于异构结构/活性数据集的计算方法。这种方法运行迅速，并且可以根据需要轻松修改。使用来自多个激酶类别的抑制剂数据生成的通用模型显示出对几个特定激酶靶点有意义的富集。这种方法可用于对化合物进行筛选优先级排序或从第三方数据收集中最佳地选择化合物。该方法的显著优势在于发现与已知活性物质结构无关的化合物，或针对没有足够信息构建特定激酶模型的新靶点。所描述的通用激酶模型主要基于酪氨酸激酶抑制剂构建，其中包含一些丝氨酸/苏氨酸激酶抑制剂；预测中使用的所有测试案例也都是针对酪氨酸激酶靶点。一旦那些生物学检测方法可用，将确定该技术对其他激酶家族的适用性。
