QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme
N. Hammoudi, Yacine Benguerba, Widad Sobhi
Abstract
Open-access reader
N. Hammoudi, Yacine Benguerba, Widad Sobhi
Abstract
Open-access reader
This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.
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This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.
Key concepts: Quantitative structure–activity relationship, Acetylcholinesterase, Enzyme inhibition, Chemistry, Linear regression, Molecular descriptor, Enzyme, Stereochemistry