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BioMed Research International
Volume 2014 (2014), Article ID 325959, 9 pages
http://dx.doi.org/10.1155/2014/325959
Research Article

An Infrastructure to Mine Molecular Descriptors for Ligand Selection on Virtual Screening

1Centro de Ciências Computacionais, Universidade Federal do Rio Grande - FURG, Avenida Itália km 8 s/n, 96203-900 Rio Grande, RS, Brazil
2Departamento de Computação Aplicada, Universidade Federal de Santa Maria - USFM, Avenida Roraima 1000, 97105-900 Santa Maria, RS, Brazil

Received 21 December 2013; Accepted 14 February 2014; Published 9 April 2014

Academic Editor: Gabriela Mustata Wilson

Copyright © 2014 Vinicius Rosa Seus et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The receptor-ligand interaction evaluation is one important step in rational drug design. The databases that provide the structures of the ligands are growing on a daily basis. This makes it impossible to test all the ligands for a target receptor. Hence, a ligand selection before testing the ligands is needed. One possible approach is to evaluate a set of molecular descriptors. With the aim of describing the characteristics of promising compounds for a specific receptor we introduce a data warehouse-based infrastructure to mine molecular descriptors for virtual screening (VS). We performed experiments that consider as target the receptor HIV-1 protease and different compounds for this protein. A set of 9 molecular descriptors are taken as the predictive attributes and the free energy of binding is taken as a target attribute. By applying the J48 algorithm over the data we obtain decision tree models that achieved up to 84% of accuracy. The models indicate which molecular descriptors and their respective values are relevant to influence good FEB results. Using their rules we performed ligand selection on ZINC database. Our results show important reduction in ligands selection to be applied in VS experiments; for instance, the best selection model picked only 0.21% of the total amount of drug-like ligands.