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Journal ArticleDOI

Exploring 2D-QSAR for prediction of beta-secretase 1 (BACE1) inhibitory activity against Alzheimer’s disease

TLDR
A robust quantitative structure–activity relationship (QSAR) model employing a dataset of 98 heterocycle compounds to identify structural features responsible for BACE1 (beta-secretase 1) enzyme inhibition is developed and it is concluded that heteroatoms present within to an aromatic nucleus and the structural features such as hydrophobic, ring aromatic and hydrogen bond acceptor/donor are responsible for the enhancement of the Bace1 enzyme inhibitory activity.
Abstract
We have developed a robust quantitative structure-activity relationship (QSAR) model employing a dataset of 98 heterocycle compounds to identify structural features responsible for BACE1 (beta-secretase 1) enzyme inhibition. We have used only 2D descriptors for model development purpose thus avoiding the conformational complications arising due to 3D geometry considerations. Following the strict Organization for Economic Co-operation and Development (OECD) guidelines, we have developed models using stepwise regression analysis followed by the best subset selection, while the final model was developed by partial least squares regression technique. The model was validated using various internationally accepted stringent validation parameters. From the insights obtained from the developed model, we have concluded that heteroatoms (nitrogen, oxygen, etc.) present within to an aromatic nucleus and the structural features such as hydrophobic, ring aromatic and hydrogen bond acceptor/donor are responsible for the enhancement of the BACE1 enzyme inhibitory activity. Moreover, we have performed the pharmacophore modelling to unveil the structural requirements for the inhibitory activity against the BACE1 enzyme. Furthermore, molecular docking studies were carried out to understand the molecular interactions involved in binding, and the results are then correlated with the requisite structural features obtained from the QSAR and pharmacophore models.

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Journal ArticleDOI

Exploring QSAR models for assessment of acute fish toxicity of environmental transformation products of pesticides (ETPPs).

TL;DR: From the developed model, lipophilicity, polarity, presence of branching and the functional form of O-atom in the transformed structures of pesticides are the important features that are to be considered during ecotoxicity assessment of ETPPs.
Journal ArticleDOI

In silico modeling for dual inhibition of acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) enzymes in Alzheimer's disease.

TL;DR: The features obtained from the 2D-QSAR modeling suggest that the number of aromatic ethers, unsaturation content relative to the molecular size and molecular shape may be more specific for the inhibition of the AChE enzyme in comparison to the BuChE enzymes.
Journal ArticleDOI

Deciphering the Interactions of Bioactive Compounds in Selected Traditional Medicinal Plants against Alzheimer's Diseases via Pharmacophore Modeling, Auto-QSAR, and Molecular Docking Approaches.

TL;DR: In this article, the authors evaluated nine flavonoid compounds identified from three medicinal plants, namely T. diversifolia, B. sapida, and I. gabonensis for their inhibitory role on acetylcholinesterase (AChE), butyrylcholine-choline (BChE) and monoamine oxidase (MAO) activity, using pharmacophore modeling, auto-QSAR prediction, and molecular studies.
Journal ArticleDOI

Design of Curcumin and Flavonoid Derivatives with Acetylcholinesterase and Beta-Secretase Inhibitory Activities Using in Silico Approaches.

TL;DR: The study indicated that, by using in silico methods, a series of curcumin and flavonoid structures were generated with promising predicted bioactivities, which could be potential candidates for further research and lead optimization.
Journal ArticleDOI

Amalgamation of in-silico, in-vitro and in-vivo approach to establish glabridin as a potential CYP2E1 inhibitor.

TL;DR: In this paper, the authors identified a potential CYP2E1 inhibitor from experimental bio-flavonoids which are unexplored for CYP 2E1 inhibition till date using in-silico, in-vitro and invivo approaches.
References
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Journal ArticleDOI

Applicability Domain for QSAR models: where theory meets reality

TL;DR: A number of approaches used to build the Applicability Domain are surveyed, focusing on strategies based on: a) physico-chemical, b) structural and c) response domains, which represents the chemical space from which a model is derived and where a prediction is considered to be reliable.
Journal ArticleDOI

Computational Modeling of β-Secretase 1 (BACE-1) Inhibitors Using Ligand Based Approaches

TL;DR: The success of the 2D descriptor based machine learning approach when compared against the 3D field based technique pursued for hBACE-1 inhibitors provides a strong impetus for systematically applying such methods during the lead identification and optimization efforts for other protein families as well.
Journal ArticleDOI

Air pollution, oxidative stress, and Alzheimer's disease.

TL;DR: Examination of the existing evidence supporting the relationship between AP, OS, and AD is examined and recommendations for future research on the population level are provided, which will provide evidence in support of public health interventions.
Journal ArticleDOI

β-Secretase as a Therapeutic Target for Alzheimer’s Disease

TL;DR: This review describes the strategy of structure-based inhibitor evolution in the development of β-secretase inhibitor drug and offers grounds for some optimism that successful disease-modifying drugs may ultimately emerge from this target.
BookDOI

Recent Advances in QSAR Studies

TL;DR: The system of this book of course will be much easier. No worry to forget bringing the recent advances in qsar studies book as discussed by the authors. But it can provide the inspiration and spirit to face this life.
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