Classification of Kinase Inhibitors Using a Bayesian Model
Citations Over TimeTop 10% of 2004 papers
Abstract
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.
Related Papers
- → O-GlcNAcylation of kinases(2012)85 cited
- → It takes two to tango – signalling by dimeric Raf kinases(2012)40 cited
- → 2-Thioxothiazolidin-4-one Analogs as Pan-PIM Kinase Inhibitors(2021)9 cited
- → The partial purification of deoxynucleoside monophosphate kinases from L cells(1965)16 cited
- → Abstract 3649: Broad profiling reveals opportunities for selective inhibition of disease-associated mutant kinases(2015)