{"title":"钠/葡萄糖协同转运体 2 抑制剂作为强效抗糖尿病药物的 QSAR 研究","authors":"Kunika Saini, Smriti Sharma","doi":"10.1134/S004057952307014X","DOIUrl":null,"url":null,"abstract":"<p>A novel class of therapeutic agents, the sodium-glucose co-transporter 2 (SGLT2) inhibitors, is emerging as a promising avenue for type 2 diabetes management. A dataset comprising 1807 SGLT2 inhibitors was subjected to a quantitative structure-activity relationship (QSAR) investigation using the AutoQSAR module of Schrodinger Maestro 12.8. Of these compounds, 1355 were designated as the training set for model development, followed by comprehensive evaluation through a battery of internal and external cross-validation techniques. Subsequently, a subset of 452 compounds served as an independent test set for external validation. The resultant QSAR model exhibited promising statistical performance, as evidenced by the calculated predicted <i>R</i><sup>2</sup> and <i>Q</i><sup>2</sup> values, at 0.873 and 0.781, respectively. Furthermore, the predictive correlation coefficient attained a commendable value of 0.84. Notably, this model demonstrates its efficacy in forecasting inhibitory activity and furnishes valuable insights that can be harnessed for the design of novel SGLT2 inhibitors in future endeavors.</p>","PeriodicalId":798,"journal":{"name":"Theoretical Foundations of Chemical Engineering","volume":"57 1 supplement","pages":"S51 - S56"},"PeriodicalIF":0.7000,"publicationDate":"2024-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"QSAR Studies of Sodium/Glucose Co-Transporter 2 Inhibitors as Potent Anti-Diabetic Drug Agents\",\"authors\":\"Kunika Saini, Smriti Sharma\",\"doi\":\"10.1134/S004057952307014X\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A novel class of therapeutic agents, the sodium-glucose co-transporter 2 (SGLT2) inhibitors, is emerging as a promising avenue for type 2 diabetes management. A dataset comprising 1807 SGLT2 inhibitors was subjected to a quantitative structure-activity relationship (QSAR) investigation using the AutoQSAR module of Schrodinger Maestro 12.8. Of these compounds, 1355 were designated as the training set for model development, followed by comprehensive evaluation through a battery of internal and external cross-validation techniques. Subsequently, a subset of 452 compounds served as an independent test set for external validation. The resultant QSAR model exhibited promising statistical performance, as evidenced by the calculated predicted <i>R</i><sup>2</sup> and <i>Q</i><sup>2</sup> values, at 0.873 and 0.781, respectively. Furthermore, the predictive correlation coefficient attained a commendable value of 0.84. Notably, this model demonstrates its efficacy in forecasting inhibitory activity and furnishes valuable insights that can be harnessed for the design of novel SGLT2 inhibitors in future endeavors.</p>\",\"PeriodicalId\":798,\"journal\":{\"name\":\"Theoretical Foundations of Chemical Engineering\",\"volume\":\"57 1 supplement\",\"pages\":\"S51 - S56\"},\"PeriodicalIF\":0.7000,\"publicationDate\":\"2024-03-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Theoretical Foundations of Chemical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://link.springer.com/article/10.1134/S004057952307014X\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, CHEMICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Theoretical Foundations of Chemical Engineering","FirstCategoryId":"5","ListUrlMain":"https://link.springer.com/article/10.1134/S004057952307014X","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
QSAR Studies of Sodium/Glucose Co-Transporter 2 Inhibitors as Potent Anti-Diabetic Drug Agents
A novel class of therapeutic agents, the sodium-glucose co-transporter 2 (SGLT2) inhibitors, is emerging as a promising avenue for type 2 diabetes management. A dataset comprising 1807 SGLT2 inhibitors was subjected to a quantitative structure-activity relationship (QSAR) investigation using the AutoQSAR module of Schrodinger Maestro 12.8. Of these compounds, 1355 were designated as the training set for model development, followed by comprehensive evaluation through a battery of internal and external cross-validation techniques. Subsequently, a subset of 452 compounds served as an independent test set for external validation. The resultant QSAR model exhibited promising statistical performance, as evidenced by the calculated predicted R2 and Q2 values, at 0.873 and 0.781, respectively. Furthermore, the predictive correlation coefficient attained a commendable value of 0.84. Notably, this model demonstrates its efficacy in forecasting inhibitory activity and furnishes valuable insights that can be harnessed for the design of novel SGLT2 inhibitors in future endeavors.
期刊介绍:
Theoretical Foundations of Chemical Engineering is a comprehensive journal covering all aspects of theoretical and applied research in chemical engineering, including transport phenomena; surface phenomena; processes of mixture separation; theory and methods of chemical reactor design; combined processes and multifunctional reactors; hydromechanic, thermal, diffusion, and chemical processes and apparatus, membrane processes and reactors; biotechnology; dispersed systems; nanotechnologies; process intensification; information modeling and analysis; energy- and resource-saving processes; environmentally clean processes and technologies.