Classification of kinase inhibitors using a Bayesian model

被引:301
作者
Xia, XY
Maliski, EG
Gallant, P
Rogers, D
机构
[1] Amgen Inc, Thousand Oaks, CA 91320 USA
[2] SciTeg, San Diego, CA 92123 USA
关键词
D O I
10.1021/jm0303195
中图分类号
R914 [药物化学];
学科分类号
100701 ;
摘要
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.
引用
收藏
页码:4463 / 4470
页数:8
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