3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Abstract

Alzheimer’s disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD.

Communicated by Ramaswamy H. Sarma

Keywords:

• Alzheimer’s disease
• quinazoline derivatives
• acetylcholinesterase
• 3D-QSAR
• pharmacophore modelling
• virtual screening
• molecular dynamics simulation

Acknowledgements

VK is thankful to the Council of Scientific and Industrial Research, New Delhi for the financial grant No. 02/(0354)/19/EMRII. VK is also thankful to the SERB-DST for the financial grant reference number EMR/2015/002339. Vijay Kumar and NK are thankful to university grant commission for providing senior research fellowship. Vinay Kumar is thankful to CSIR for providing NET-JRF fellowship. Kailash is thankful to ICMR for the SRF-project File No. 45/29/2022-/BIO/BMS.

Disclosure statement

Authors declare no potential conflict of interest.

Author contributions

Vijay Kumar and VK have designed the entire study, and were also involved in the conceptualisation and investigation. Vijay Kumar and Kailash performed Virtual screening, MD simulations and DFT study, and prepared the manuscript. Vinay Kumar and Naveen Kumar have contributed towards pharmacokinetic and molecular docking. All the authors have approved the manuscript.

Additional information

Funding

This work was supported by Council of Scientific and Industrial Research, New Delhi for the financial grant No. 02/(0354)/19/EMRII and SERB-DST for the financial grant reference number EMR/2015/002339.