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Drug Discovery and Design for Complex Diseases through QSAR Computational Methods

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

Document Type: Research Article

Affiliations: Department of Information and Communication Technologies, Computer Science Faculty, University of A Coruna, Campus de Elvina, S/N, 15071 A Coruna, Spain.

Publication date: August 1, 2010

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  • Current Pharmaceutical Design publishes timely in-depth reviews covering all aspects of current research in rational drug design. Each issue is devoted to a single major therapeutic area. A Guest Editor who is an acknowledged authority in a therapeutic field has solicits for each issue comprehensive and timely reviews from leading researchers in the pharmaceutical industry and academia.

    Each thematic issue of Current Pharmaceutical Design covers all subject areas of major importance to modern drug design, including: medicinal chemistry, pharmacology, drug targets and disease mechanism.
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