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ISSN: 2524-2172 (Print)
ISSN: 2524-2180 (Online)
Research Article Open Access

QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme

Nour El Houda Hammoudi1, Yacine Benguerba1 and Widad Sobhi1
  • 1 Université Ferhat ABBAS Sétif-1, Algeria

Abstract

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.

Current Research in Bioinformatics
Volume 8 No. 1, 2019, 62-65

DOI: https://doi.org/10.3844/ajbsp.2019.62.65

Submitted On: 25 December 2019 Published On: 2 March 2020

How to Cite: Hammoudi, N. E. H., Benguerba, Y. & Sobhi, W. (2019). QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme. Current Research in Bioinformatics, 8(1), 62-65. https://doi.org/10.3844/ajbsp.2019.62.65

Open AccessCopyright: © 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.

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Keywords

  • QSAR Model
  • Acetylcholinesterase
  • Alzheimer Disease
  • MLR Model