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Current Computer-Aided Drug Design

Editor-in-Chief

ISSN (Print): 1573-4099
ISSN (Online): 1875-6697

Research Article

Molecular Docking, Drug-Likeness and ADMET Analysis, Application of Density Functional Theory (DFT) and Molecular Dynamics (MD) Simulation to the Phytochemicals from Withania Somnifera as Potential Antagonists of Estrogen Receptor Alpha (ER- α)

Author(s): Alamgir Hossain*

Volume 17, Issue 6, 2021

Published on: 30 July, 2020

Page: [797 - 805] Pages: 9

DOI: 10.2174/1573409916999200730181611

Price: $65

Abstract

Introduction: Breast cancer is one of the leading causes of death of women every year. Estrogen receptor alpha (ER- α) is an important pathway that is responsible for the development of breast cancer. Tamoxifen is the most commonly used drug to treat breast cancer. But the main drawback of using this drug is that it increases the risk of uterine cancer, stroke, and pulmonary embolism.

Methods: In this research, the in-silico approach was followed to get the anticancer agent from Withania somnifera as the root extract of the plant is active against breast cancer. For this, 15 bioactive molecules were subjected to molecular docking and 9 molecules were obtained comparing the consensus binding affinity of H3B-9224.

Results: After rescoring, drug-likeness analysis and ADMET analysis of the molecules were carried out and 3 molecules remained. These 3 molecules showed good ADMET properties, which are crucial requirements in the drug discovery process. Their activity was checked by applying density functional theory (DFT) and all of them showed good reactivity. Their binding interaction was also evaluated.

Conclusion: Finally, the stability of those molecules was evaluated by applying molecular dynamics (MD) simulation. After this simulation, 2 molecules remained that had good stability with the protein during the simulation period.

Keywords: Breast cancer, molecular docking, drug-likeness, ADMET, density functional theory, molecular dynamics (MD) simulation, withania somnifera.

Graphical Abstract


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