Abstract
Alzheimer’s disease is one of the most common neurodegenerative disorders in elder population. The β-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.
Highlights
Ample experimental studies about the hallmarks of AD conclude that deposition of amyloid plaques in the brains of Alzheimer’s patients constitutes one of the crucial causes of the disease progression[3,4]
A database with 215 molecules was assembled, where the half maximal inhibitory concentration (IC50) values of the compounds were extracted from the literature and web servers
Several studies proposed that BACE1 inhibitors have high therapeutic potential for decelerating the long-term progression of AD, and during last decade several quantitative structure-activity relationships (QSAR) models have been proposed in the literature
Summary
Ample experimental studies about the hallmarks of AD conclude that deposition of amyloid plaques in the brains of Alzheimer’s patients constitutes one of the crucial causes of the disease progression[3,4]. Several authors have performed QSAR approaches based on target properties that are correlated to the most important ligand-target interactions[14] used PoseView[25,26] and Ligand Explorer software[27] to find the most important ligand-target interactions of structures deposited in the PDB Bank[28] visualizing and adjusting the best distance between the atom’s interactions In this sense, two descriptors were obtained by the last procedure to predict the activity of BACE1 inhibitory compounds, the hydrophobic contacts at 4–5 Å and the number of hydrogen bonds between ligand and target
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