Classification of Kinase Inhibitors Using a Bayesian Model

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.

Novel Bayesian classification models for predicting compounds blocking hERG potassium channels

“…When n features of a compound were generated, a cumulative score of feature contributions to the actives likeness (P combined ) was computed. More details concerning the method have been described by Xia et al [26] .…”

Evaluation of machine-learning methods for ligand-based virtual screening

“…Substructural analysis was studied in considerable detail by workers at the National Institutes of Health in an extended programme to develop novel anti-cancer agents [47][48][49], and also by workers at Lederle [29] and Sheffield [50][51][52]. However, it is only in the last few years that this general approach has become widely used [53][54][55][56][57][58][59][60][61][62].…”

Calculation of Substructural Analysis Weights Using a Genetic Algorithm

“…Experiments were also conducted using the NBC in the Pipeline Pilot software [19][20][21] ; however the results obtained were comparable to those with R4 and hence only the latter sets of results are discussed here.…”

“…An operational example of the use of substructural analysis is the PASS (for Prediction of Activity Spectra for Substances) system developed by the Poroikov group 11,16,17 . Some of the weighting schemes that have been used in substructural analysis are closely related to those obtained using a naive Bayesian classifier 18 (hereafter NBC), a well-established approach to machine learning that has become popular in chemoinformatics with the availability of the Bayesian modelling routine in the Pipeline Pilot software system [19][20][21] .…”