Drug Discovery and Design for Complex Diseases through QSAR Computational Methods

Title: Drug Discovery and Design for Complex Diseases through QSAR Computational Methods

Volume: 16 Issue: 24

Author(s): Cristian R. Munteanu, Enrique Fernandez-Blanco, Jose A. Seoane, Pilar Izquierdo-Novo, Jose Angel Rodriguez-Fernandez, Jose Maria Prieto-Gonzalez, Juan R. Rabunal and Alejandro Pazos

Affiliation:

Keywords: Drug design, QSAR, graphs, complex network, complex disease, QSAR Computational Methods, Drug Discovery, biochemical networks, drug-target interactions, protein-protein interaction networks, protein folding kinetics, (QPDRs), physio-chemical properties, Neurology, Cardiology, Oncology, QSAR models, TIs/CIs, information indices, MARCHINSIDE

Abstract: There is a need for the study of complex diseases due to their important impact on our society. One of the solutions involves the theoretical methods which are fast and efficient tools that can lead to the discovery of new active drugs specially designed for these diseases. The Quantitative Structure - Activity Relationship models (QSAR) and the complex network theory become important solutions for screening and designing efficient pharmaceuticals by coding the chemical information of the molecules into molecular descriptors. This review presents the most recent studies on drug discovery and design using QSAR of several complex diseases in the fields of Neurology, Cardiology and Oncology.

Cite this article as:

R. Munteanu Cristian, Fernandez-Blanco Enrique, A. Seoane Jose, Izquierdo-Novo Pilar, Angel Rodriguez-Fernandez Jose, Maria Prieto-Gonzalez Jose, R. Rabunal Juan and Pazos Alejandro, Drug Discovery and Design for Complex Diseases through QSAR Computational Methods, Current Pharmaceutical Design 2010; 16 (24) . https://dx.doi.org/10.2174/138161210792389252