High-prediction QSAR Modeling Study Based on the Efficacy of a Novel 6-hydroxybenzothiazole-2-carboxamide Targeted Monoamine Oxidase B in the Treatment of Neurodegenerative Diseases.

BACKGROUND

Neurodegenerative diseases are a group of disorders characterized by progressive neuronal degeneration and death, of which Alzheimer's disease and Parkinson's disease are the most common. These diseases are closely associated with increased expression of monoamine oxidase B (MAO-B), an important enzyme that regulates neurotransmitter concentration, and its overactivity leads to oxidative stress and neurotoxicity, accelerating the progression of neurodegenerative diseases. Therefore, the development of effective MAO-B inhibitors is important for the treatment of neurodegenerative diseases.

OBJECTIVE

This study aims to improve the prediction of the efficacy of novel 6-hydroxybenzothiazole- 2-carboxamide compounds in inhibiting MAO-B by improving the quantitative constitutive effect relationship (QSAR) modeling and to provide a theoretical basis for the discovery of novel neuroprotective drugs.

METHODS

The study first optimized the structures of 36 compounds using the heuristic method (HM) in CODESSA software to construct linear QSAR models. Subsequently, key descriptors were screened by using the gene expression programming (GEP) technique to generate nonlinear QSAR models and validate them.

RESULTS

The R², F-value, and R²cv of the linear model were 0.5724, 10.3752, and 0.4557, respectively, whereas the nonlinear model constructed by the GEP algorithm showed higher prediction accuracies by achieving R² values of 0.89 and 0.82, and mean squared errors (MSE) of 0.0799 and 0.1215 for the training and test sets, respectively. In addition, molecular docking experiments confirmed that the novel compound 31 was tightly bound to the MAO-B active site with significant inhibitory activity.

CONCLUSION

In this study, we successfully improved the prediction ability of the efficacy of novel 6-hydroxybenzothiazole-2-carboxamide compounds to inhibit MAO-B by improving the QSAR model. This not only provides new drug candidates for the treatment of neurodegenerative diseases, but also provides important theoretical guidance for subsequent drug design and development, which can help accelerate the process of new drug discovery and reduce the disease burden of patients.