Huan Wang, Shengnan Ren, Liyang Sun, H. Si, Zhuang Yu
{"title":"吡唑氮杂环[3.2.1]辛烷磺酰胺类n -酰基乙醇胺水解酸酰胺酶抑制剂的QSAR研究","authors":"Huan Wang, Shengnan Ren, Liyang Sun, H. Si, Zhuang Yu","doi":"10.2174/1570180820666230418093238","DOIUrl":null,"url":null,"abstract":"\n\nInflammation 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.\n\n\n\nWe predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds.\n\n\n\nwe 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.\n\n\n\nThe 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.\n\n\n\nFrom 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.\n","PeriodicalId":18063,"journal":{"name":"Letters in Drug Design & Discovery","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-Acylethanolamine-Hydrolyzing Acid Amidase Inhibitors\",\"authors\":\"Huan Wang, Shengnan Ren, Liyang Sun, H. Si, Zhuang Yu\",\"doi\":\"10.2174/1570180820666230418093238\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nInflammation 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.\\n\\n\\n\\nWe predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds.\\n\\n\\n\\nwe 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.\\n\\n\\n\\nThe 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.\\n\\n\\n\\nFrom 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.\\n\",\"PeriodicalId\":18063,\"journal\":{\"name\":\"Letters in Drug Design & Discovery\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Letters in Drug Design & Discovery\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1570180820666230418093238\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Letters in Drug Design & Discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1570180820666230418093238","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QSAR Studies on a Series of Pyrazole Azabicyclo [3.2.1] Octane Sulfonamides N-Acylethanolamine-Hydrolyzing Acid Amidase Inhibitors
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.
We predict the activity of the N-acylethanolamine-hydrolyzing acid amidase (NAAA) inhibitor to find more effective compounds.
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.
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.
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.