Integrating statistical predictions and experimental verifications for enhancing protein-chemical interaction predictions in virtual screening.

ABSTRACT: Predictions of interactions between target proteins and potential leads are of great benefit in the drug discovery process. We present a comprehensively applicable statistical prediction method for interactions between any proteins and chemical compounds, which requires only protein sequence data and chemical structure data and utilizes the statistical learning method of support vector machines. In order to realize reasonable comprehensive predictions which can involve many false positives, we propose two approaches for reduction of false positives: (i) efficient use of multiple statistical prediction models in the framework of two-layer SVM and (ii) reasonable design of the negative data to construct statistical prediction models. In two-layer SVM, outputs produced by the first-layer SVM

SUBMITTER: Nagamine N 

PROVIDER: S-EPMC2685987 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature