A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands.

ABSTRACT: Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%-88.

SUBMITTER: Zhang J 

PROVIDER: S-EPMC3376116 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature