Predicting a small molecule-kinase interaction map: A machine learning approach.

ABSTRACT:

Background

We present a machine learning approach to the problem of protein ligand interaction prediction. We focus on a set of binding data obtained from 113 different protein kinases and 20 inhibitors. It was attained through ATP site-dependent binding competition assays and constitutes the first available dataset of this kind. We extract information about the investigated molecules from various data sources to obtain an informative set of features.

Results

A Support Vector Machine (SVM) as well as a decision tree algorithm (C5/See5) is used to learn models based on the available features which in turn can be used for the classification of new kinase-inhibitor pair test instances. We evaluate our approach using different feature sets and parameter settings for the employed

SUBMITTER: Buchwald F 

PROVIDER: S-EPMC3151211 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature