{"title":"新型预测QSAR模型与分子对接:抗SARS-Cov-2潜在关注的新型天然磺胺类药物","authors":"Nathalie Moussa, Hoda Mando","doi":"10.2174/2211352521666230717115823","DOIUrl":null,"url":null,"abstract":"\n\nSince the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vaccines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules.\n\n\n\nIn this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity\n\n\n\nThe study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was validated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Furthermore, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation.\n\n\n\nOur findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.\n","PeriodicalId":7951,"journal":{"name":"Anti-Infective Agents","volume":" ","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Novel and Predictive QSAR Model and Molecular Docking: New Natural Sulfonamides of Potential Concern Against SARS-Cov-2\",\"authors\":\"Nathalie Moussa, Hoda Mando\",\"doi\":\"10.2174/2211352521666230717115823\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n\\nSince the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vaccines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules.\\n\\n\\n\\nIn this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity\\n\\n\\n\\nThe study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was validated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Furthermore, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation.\\n\\n\\n\\nOur findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.\\n\",\"PeriodicalId\":7951,\"journal\":{\"name\":\"Anti-Infective Agents\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Anti-Infective Agents\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/2211352521666230717115823\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anti-Infective Agents","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/2211352521666230717115823","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
Novel and Predictive QSAR Model and Molecular Docking: New Natural Sulfonamides of Potential Concern Against SARS-Cov-2
Since the outbreak of the COVID-19 pandemic in 2019, the world has been racing to develop effective drugs for treating this deadly disease. Although there are now some vaccines that have somewhat alleviated global panic, the lack of approved drugs remains a persistent challenge. Consequently, there is a pressing need to discover new therapeutic molecules.
In this study, we explore the application of a quantitative structure−activity relationship (QSAR) model to predict the efficacy of 28 cyclic sulfonamide derivatives against SARS-CoV-2. The model was developed using multiple linear regression, and six molecular descriptors were identified as the most significant factors in determining the inhibitory activity. This proposed QSAR model holds the potential for aiding the virtual screening and drug design process in the development of new and more effective SARS-CoV-2 inhibitors. The model was also applied to seven natural products primary sulfonamides and sulfamates, demonstrating promising activity
The study results indicated that the atom count, as represented by the descriptor nCl, had the most significant impact on the inhibitory activity against SARS-CoV-2. The proposed model was validated using various statistical parameters, confirming its validity, robustness, and predictiveness, with a high correlation coefficient (R2) of 0.77 for the training group and 0.95 for the test group. Furthermore, we predicted the activity of seven natural compounds, and among them, Dealanylascamycin exhibited the highest predicted activity. Subsequently, Dealanylascamycin was docked to SARS-CoV-2 and the results of the docking study further strengthened its potential as a promising candidate against COVID-19, suggesting that it should be considered for further optimization and validation.
Our findings demonstrate promising predicted inhibitory activity against SARS-CoV-2 for seven natural products, primary sulfonamides, and primary sulfamates.
期刊介绍:
Anti-Infective Agents publishes original research articles, full-length/mini reviews, drug clinical trial studies and guest edited issues on all the latest and outstanding developments on the medicinal chemistry, biology, pharmacology and use of anti-infective and anti-parasitic agents. The scope of the journal covers all pre-clinical and clinical research on antimicrobials, antibacterials, antiviral, antifungal, and antiparasitic agents. Anti-Infective Agents is an essential journal for all infectious disease researchers in industry, academia and the health services.