Improving structural similarity based virtual screening using background knowledge.

ABSTRACT:

Background

Virtual screening in the form of similarity rankings is often applied in the early drug discovery process to rank and prioritize compounds from a database. This similarity ranking can be achieved with structural similarity measures. However, their general nature can lead to insufficient performance in some application cases. In this paper, we provide a link between ranking-based virtual screening and fragment-based data mining methods. The inclusion of binding-relevant background knowledge into a structural similarity measure improves the quality of the similarity rankings. This background knowledge in the form of binding relevant substructures can either be derived by hand selection or by automated fragment-based data mining methods.

Results

In virtual screening

SUBMITTER: Girschick T 

PROVIDER: S-EPMC3928642 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature