Name: Computational Approaches for Identifying Drugs Against Alzheimer's Disease (en Inglés)
SKU: 62474008
Price: 815.50 MXN
Availability: InStock
Author: Radha Mahendran; Suganya Jeyabaskar; Astral Gabriella Francis
ISBN: 9783960671381

portada Computational Approaches for Identifying Drugs Against Alzheimer's Disease (en Inglés)

Computational Approaches for Identifying Drugs Against Alzheimer's Disease (en Inglés)

Formato

Libro Físico

Editorial

Anchor Academic Publishing

Autor

Radha Mahendran; Suganya Jeyabaskar; Astral Gabriella Francis

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

ISBN13

9783960671381

Computational Approaches for Identifying Drugs Against Alzheimer's Disease (en Inglés)

Radha Mahendran; Suganya Jeyabaskar; Astral Gabriella Francis (Autor) · Anchor Academic Publishing · Tapa Blanda

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Reseña del libro "Computational Approaches for Identifying Drugs Against Alzheimer's Disease (en Inglés)"

Alzheimer's disease is the most common form of dementia which is incurable. Although some kinds of memory loss are normal during aging, these are not severe enough to interfere with the level of function. -Secretase is an important protease in the pathogenesis of Alzheimer's disease. Some statine-based peptidomimetics show inhibitory activities to the -secretase. To explore the inhibitory mechanism, molecular docking and three-dimensional quantitative structure-activity relationship (3D-QSAR) studies on these analogues were performed. Quantitative structure-activity relationship (QSAR) modeling pertains to the construction of predictive models of biological activities as a function of structural and molecular information of a compound library. The concept of QSAR has typically been used for drug discovery and development and has gained wide applicability for correlating molecular information with not only biological activities but also with other physicochemical properties, which has therefore been termed quantitative structure-property relationship (QSPR). In this study, 3D QSAR and pharmacophore mapping studies were carried out using Accelrys Discovery Studio 2.1. The best nine drugs were selected from the 16 ligands and pharmacophore features were generated.