QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme
Nour El Houda Hammoudi, Yacine Benguerba and Widad Sobhi
DOI : 10.3844/ajbsp.2019.62.65
Current Research in Bioinformatics
Volume 8, 2019
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.
© 2019 Nour El Houda Hammoudi, Yacine Benguerba and Widad Sobhi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.