2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors

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Ghasemi Dogaheh Mahtab; Ebrahimi Najafabadi Hesmatollah; YOUSEFBEYK FATEMEH; ABOLGHASEMI SAEED

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Journal Paper

Paper Information

Journal: Koomesh Year:2022 | Volume:24 | Issue:3 (89) Page(s): 405-420

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Information Journal Paper

Title

2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors

Author(s)

Ghasemi Dogaheh Mahtab | Ebrahimi Najafabadi Hesmatollah | YOUSEFBEYK FATEMEH | ABOLGHASEMI SAEED | Issue Writer Certificate

Keywords

Quantitative Structure-Activity Relationship

ErbB Receptors

Regression Analysis

Support Vector Machine

Neural Networks

Abstract

Introduction: Epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor derivatives play an important role in the treatment of cancer. We aim to construct 2D-QSAR models using various chemometrics using 4anilinoquinazoline-containing EGFR TKIs. In addition, the binding profile of these compounds was evaluated using a docking study. Materials and Methods: In this study, 122 compounds of seven different structural categories with a 4anilinoquinazoline scaffold were obtained from the pieces of literature. 2D-QSAR models were prepared using the linear method, including multiple linear regression (MLR), alongside non-linear methods, including artificial Neural Networks (ANN) and Support Vector Machines (SVM). The validation of suggested 2D-QSAR models was performed using internal and external validation techniques. For molecular docking, three compounds, including compounds 32, 75, and 98, which had the highest pIC50, were used. Molecular docking was performed using AutoDock 4. 2. Results: Selected descriptors indicated that atomic electronegativity, hydrogen bonding ability, lipophilicity, and molecular shape and volume were the factors affecting the activity of the compounds. Statistical criteria, related to interpretation and validation of the models, were in the appropriate range. The model obtained by the ANN method, with the lowest mean absolute error of 0. 365 for both training and testing sets, was the best. The compound 75 as the most potent compound indicated binding energies of-8. 33 kcal/mol. Conclusion: Finally, it was found that the models could effectively predict the activity of the compounds. It was also discovered that the compounds were properly bound to the active site of the receptor. In addition, validation results showed that all processes were sufficiently valid.

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APA: Copy

Ghasemi Dogaheh, Mahtab, Ebrahimi Najafabadi, Hesmatollah, YOUSEFBEYK, FATEMEH, & ABOLGHASEMI, SAEED. (2022). 2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors. KOOMESH, 24(3 (89) ), 405-420. SID. https://sid.ir/paper/1059096/en

Vancouver: Copy

Ghasemi Dogaheh Mahtab, Ebrahimi Najafabadi Hesmatollah, YOUSEFBEYK FATEMEH, ABOLGHASEMI SAEED. 2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors. KOOMESH[Internet]. 2022;24(3 (89) ):405-420. Available from: https://sid.ir/paper/1059096/en

IEEE: Copy

Mahtab Ghasemi Dogaheh, Hesmatollah Ebrahimi Najafabadi, FATEMEH YOUSEFBEYK, and SAEED ABOLGHASEMI, “2D-QSAR and docking studies of 4-anilinoquinazoline derivatives as epidermal growth factor receptor tyrosine kinase inhibitors,” KOOMESH, vol. 24, no. 3 (89) , pp. 405–420, 2022, [Online]. Available: https://sid.ir/paper/1059096/en

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