Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer’s disease treatment

ABSTRACT

The enzyme acetylcholinesterase (AChE) is currently a therapeutic target for the treatment of neurodegenerative diseases. These diseases have highly variable causes but irreversible evolutions. Although the treatments are palliative, they help relieve symptoms and allow a better quality of life, so the search for new therapeutic alternatives is the focus of many scientists worldwide. In this study, a QSAR-SVM classification model was developed by using the MATLAB numerical computation system and the molecular descriptors implemented in the Dragon software. The obtained parameters are adequate with accuracy of 88.63% for training set, 81.13% for cross-validation experiment and 81.15% for prediction set. In addition, its application domain was determined to guarantee the reliability of the predictions. Finally, the model was used to predict AChE inhibition by a group of quinazolinones and benzothiadiazine 1,1-dioxides obtained by chemical synthesis, resulting in 14 drug candidates with in silico activity comparable to acetylcholine.

KEYWORDS:

• Acetylcholinesterase inhibitor
• Alzheimer’s disease
• benzothiadiazine 1
• 1-dioxide
• quinazolinones
• support vector machine

Acknowledgements

J.A. Castillo-Garit thanks the program “Estades Temporals per a Investigadors Convidats” for a fellowship to work at Valencia University in 2021. We acknowledge the principal financial support from the National Foundation for Science and Technology of Vietnam (NAFOSTED, Grant number 104.01-2018.301). F. Torrens acknowledges financial support from an internal aid from Universidad Católica de Valencia San Vicente Mártir.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplementary material

Supplemental data for this article can be accessed at: https://doi.org/10.1080/1062936X.2022.2025615.

Additional information

Funding

This work was supported by the Vietnam National Foundation for Science and Technology Development (NAFOSTED) [104.01-2018.301].