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Ligand-based discovery of new potential acetylcholinesterase inhibitors for Alzheimer's disease treatment
被引:4
|作者:
Canizares-Carmenate, Y.
[1
]
Nam, N-H
[2
]
Diaz-Amador, R.
[3
]
Thuan, N. T.
[2
]
Dung, P. T. P.
[2
]
Torrens, F.
[4
]
Pham-The, H.
[2
]
Perez-Gimenez, F.
[5
]
Castillo-Garit, J. A.
[5
,6
]
机构:
[1] Univ Cent Marta Abreu Las Villas, Fac Quim Farm, Unit Comp Aided Mol Biosil Discovery & Bioinforma, Santa Clara, Cuba
[2] Hanoi Univ Pharm, Dept Pharmaceut Chem, Hanoi, Vietnam
[3] Univ Cent Marta Abreu Las Villas, Dept Comp Sci, Santa Clara, Cuba
[4] Univ Valencia, Inst Univ Ciencia Mol, Edifici Inst Paterna, Valencia, Spain
[5] Univ Valencia, Fac Farm, Dept Quim Fis, Unidad Invest Diseno Farmacos & Conectividad Mol, Valencia, Spain
[6] Univ Ciencias Med Villa Clara, Unidad Toxicol Expt, Santa Clara, Cuba
关键词:
Acetylcholinesterase inhibitor;
Alzheimer's disease;
benzothiadiazine;
1;
1-dioxide;
quinazolinones;
support vector machine;
DRUG DESIGN;
CLASSIFICATION;
D O I:
10.1080/1062936X.2022.2025615
中图分类号:
O6 [化学];
学科分类号:
0703 ;
摘要:
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.
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页码:49 / 61
页数:13
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