Abstract
Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD treatments is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD, constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitative-structure-activity relationships (QSAR). The principal purpose was the in silico design of multitarget (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt- QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins.
Keywords: Anti-AD agents, fragment, linear discriminant analysis, mt-inhibitors, QSAR, quantitative contributions
Current Alzheimer Research
Title:Multi-Target Inhibitors for Proteins Associated with Alzheimer: In Silico Discovery using Fragment-Based Descriptors
Volume: 10 Issue: 2
Author(s): Alejandro Speck-Planche, Valeria V. Kleandrova, Feng Luan and M. Natalia D. S. Cordeiro
Affiliation:
Keywords: Anti-AD agents, fragment, linear discriminant analysis, mt-inhibitors, QSAR, quantitative contributions
Abstract: Alzheimer disease (AD) is one of the most common and serious neurodegenerative disorders in humans. For this reason, the search for new anti-AD treatments is a very active area. Only few biological receptors associated with AD have been well studied. The efficacy of the current drugs is limited by the fact that they inhibit only one target like protein. Thus, the rational design of new drug candidates as versatile inhibitors for different proteins associated with AD, constitutes a major goal. With the aim to overcome this problem, we developed here the first fragment-based approach by exploring quantitative-structure-activity relationships (QSAR). The principal purpose was the in silico design of multitarget (mt) inhibitors against five proteins associated with AD. Our approach was focused on the construction of an mt- QSAR discriminant model using a large and heterogeneous database of compounds and substructural descriptors, which permitted the simultaneous classification and prediction of inhibitors against five proteins associated with AD. The model correctly classified more than 90% of active and inactive compounds in both, training and prediction series. As principal advantage, this mt-QSAR discriminant model was used for the automatic and fast extraction of fragments responsible for the inhibitory activity against the five proteins under study, and new molecular entities were suggested as possible versatile inhibitors for these proteins.
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Cite this article as:
Speck-Planche Alejandro, V. Kleandrova Valeria, Luan Feng and Natalia D. S. Cordeiro M., Multi-Target Inhibitors for Proteins Associated with Alzheimer: In Silico Discovery using Fragment-Based Descriptors, Current Alzheimer Research 2013; 10 (2) . https://dx.doi.org/10.2174/1567205011310020001
DOI https://dx.doi.org/10.2174/1567205011310020001 |
Print ISSN 1567-2050 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5828 |
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Aims and Scope: Introduction: Alzheimer's disease (AD) poses a significant global health challenge, with an increasing prevalence that demands concerted efforts to advance our understanding and strategies for prevention, diagnosis, treatment, and rehabilitation. This thematic issue aims to bring together cutting-edge research and innovative approaches from multidisciplinary perspectives to address ...read more
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Alzheimer's disease (AD) poses a significant global health challenge, with an increasing number of individuals affected yearly. Deep learning, a subfield of artificial intelligence, has shown immense potential in various domains, including healthcare. This thematic issue of Current Alzheimer Research explores the application of deep learning techniques in advancing our ...read more
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