Computational Approaches for Identifying Drugs Against Alzheimer's Disease
(Sprache: Englisch)
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. ß-Secretase is an important protease in the...
Voraussichtlich lieferbar in 3 Tag(en)
versandkostenfrei
Buch (Kartoniert)
39.99 €
- Lastschrift, Kreditkarte, Paypal, Rechnung
- Kostenlose Rücksendung
Produktdetails
Produktinformationen zu „Computational Approaches for Identifying Drugs Against Alzheimer's Disease “
Klappentext zu „Computational Approaches for Identifying Drugs Against Alzheimer's Disease “
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. ß-Secretase is an important protease in the pathogenesis of Alzheimer's disease. Some statine-based peptidomimetics show inhibitory activities to the ß-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.
Lese-Probe zu „Computational Approaches for Identifying Drugs Against Alzheimer's Disease “
Text Sample:Chapter 5: METHODOLOGY:
QUANTITATIVE STRUCTURE-ACTIVITY RELATIONSHIP models (QSAR models) are regression or classification models used in the chemical and biological sciences and engineering. Like other regression models, QSAR regression models relate a set of "predictor" variables (X) to the potency of the response variable (Y), while classification QSAR models relate the predictor variables to a categorical value of the response variable. In QSAR modeling, the predictors consist of physico-chemical properties or theoretical molecular descriptors of chemicals; the QSAR response-variable could be a biological activity of the chemicals. QSAR models first summarize a supposed relationship between chemical structures and biological activity in a data-set of chemicals. Second, QSAR models predict the activities of new chemicals (31, 32).
EVALUATION OF THE QUALITY OF QSAR MODELS:
QSAR modeling produces predictive models derived from application of statistical tools correlating biological activity (including desirable therapeutic effect and undesirable side effects) or physico-chemical properties in QSPR models of chemicals (drugs/toxicants/environmental pollutants) with descriptors representative of molecular structure or properties. QSARs are being applied in many disciplines, for example: risk assessment, toxicity prediction, and regulatory decisions in addition to drug discovery and lead optimization. Obtaining a good quality QSAR model depends on many factors, such as the quality of input data, the choice of descriptors and statistical methods for modeling and for validation. Any QSAR modeling should ultimately lead to statistically robust and predictive models capable of making accurate and reliable predictions of the modeled response of new compounds. For validation of QSAR models, usually various strategies are adopted:
(i) internal validation or cross-validation (actually, while extracting data, cross validation is a measure of model robustness,
... mehr
the more a model is robust (higher q2) the less data extraction perturb the original model).
(ii) external validation by splitting the available data set into training set for model development and prediction set for model predictivity check.
(iii) blind external validation by application of model on new external data and.
(iv) data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors.
The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain (AD) of the models. Some validation methodologies can be problematic. Different aspects of validation of QSAR models that need attention includes methods of selection of training set compounds, setting training set size and impact of variable selection for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important (33).
DEVELOPMENT OF QSAR MODEL:
The construction of QSAR model typically comprises of two main steps: (i) description of molecular structure and (ii) multivariate analysis for correlating molecular descriptors with observed activities/properties. An essential preliminary step in model development is data understanding. Intermediate steps that are also crucial for successful development of such QSAR models include data pre processing and statistical evaluation [...].
DATA UNDERSTANDING:
Data understanding is a crucial step that one should not overlook as it helps the researcher to become familiar with the nature of the data prior to actual QSAR model construction thereby reducing unnecessar
(ii) external validation by splitting the available data set into training set for model development and prediction set for model predictivity check.
(iii) blind external validation by application of model on new external data and.
(iv) data randomization or Y-scrambling for verifying the absence of chance correlation between the response and the modeling descriptors.
The success of any QSAR model depends on accuracy of the input data, selection of appropriate descriptors and statistical tools, and most importantly validation of the developed model. Validation is the process by which the reliability and relevance of a procedure are established for a specific purpose; for QSAR models validation must be mainly for robustness, prediction performances and applicability domain (AD) of the models. Some validation methodologies can be problematic. Different aspects of validation of QSAR models that need attention includes methods of selection of training set compounds, setting training set size and impact of variable selection for training set models for determining the quality of prediction. Development of novel validation parameters for judging quality of QSAR models is also important (33).
DEVELOPMENT OF QSAR MODEL:
The construction of QSAR model typically comprises of two main steps: (i) description of molecular structure and (ii) multivariate analysis for correlating molecular descriptors with observed activities/properties. An essential preliminary step in model development is data understanding. Intermediate steps that are also crucial for successful development of such QSAR models include data pre processing and statistical evaluation [...].
DATA UNDERSTANDING:
Data understanding is a crucial step that one should not overlook as it helps the researcher to become familiar with the nature of the data prior to actual QSAR model construction thereby reducing unnecessar
... weniger
Bibliographische Angaben
- Autoren: Radha Mahendran , Suganya Jeyabaskar , Astral Gabriella Francis
- 2017, 72 Seiten, 27 Abbildungen, Maße: 15,5 x 22 cm, Kartoniert (TB), Englisch
- Verlag: Anchor Academic Publishing
- ISBN-10: 3960671385
- ISBN-13: 9783960671381
Sprache:
Englisch
Kommentar zu "Computational Approaches for Identifying Drugs Against Alzheimer's Disease"
Schreiben Sie einen Kommentar zu "Computational Approaches for Identifying Drugs Against Alzheimer's Disease".
Kommentar verfassen