Docking study and QSAR analysis based on the artificial neural network and multiple linear regression of novel harmine derivatives
by Taoufik Akabli; Hamid Toufik; Mourad Stitou; Fatima Lamchouri
International Journal of Computer Aided Engineering and Technology (IJCAET), Vol. 18, No. 1/2/3, 2023

Abstract: Harmine and its derivatives are an important class of natural molecules for fighting cancer. Researching for the physical characteristics involved in this activity provides crucial keys to develop new derivatives which are more active and less toxic. For this purpose, a series of 50 harmine derivatives were studied using molecular modelling, namely 2D-QSAR analysis and molecular docking. The best 2D-QSAR model was developed correlating the three most important descriptors with the cytotoxic activity using MLR and ANN. The statistical analysis indicates high performance of the established models (R²MLR = 0.77, q²MLR = 0.73, R²_extMLR = 0.81, Q²F3MLR = 0.70, r²m_MLR = 0.71 and CCC_MLR = 0.88, R²ANN = 0.86, q²ANN = 0.79 and R²extANN = 0.76). The analysis of the selected three descriptors showed that the lipophilicity remains the crucial property on which cytotoxic activity depends. Moreover, molecular docking of the most active compound (44) shows that it takes up a good pose into the active site of DYRKA1 kinase, as reflected by the low binding energy (-10.5 kcal/mol) and the various interactions formed with the amino acids. Thus, these results were exploited to design six new derivatives having high predicted pIC50, low binding energy and exciting pharmacokinetics properties.

Online publication date: Mon, 19-Dec-2022

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Aided Engineering and Technology (IJCAET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com