QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-Acylethanolamine-Hydrolyzing Acid Amidase Inhibitors

Abstract

Background: Inflammation is a common and intractable disease for humans. Current anti-inflammatory drugs have a lot of side effects, which cause irreversible damage to the body. Objective: We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds. Methods: we established a quantitative structure-activity relationship (QSAR) model by gene expression programming to predict the IC50 values of natural compounds. The NAAA inhibitor, as a cysteine enzyme, plays an important role in the therapy of pain, anti-inflammatory effects and application of other diseases. A total of 36 NAAA inhibitors were optimized by the heuristic method in the CODESSA program to build a linear model. The 27 compounds and 9 compounds were in train and test sets. On this basis, we selected three descriptors and used them to build nonlinear models in gene expression programming. Results: The best model in the gene expression programming method was found, the square of correlation coefficients of R2 and mean square error for the training set were 0.79 and 0.14, testing set was 0.78 and 0.20, respectively. Conclusion: From this method, the activity of molecules could be predicted, and the best method was found. Therefore, this model has a stronger predictive ability to develop NAAA inhibitors.