{"title":"3D-QSAR studies of some [[1-aryl(or benzyl)-1-(benzenesulphonamido)methyl] phenyl] alkanoic acid derivatives as thromboxane A2 receptor antagonists.","authors":"K. V. Sairam, J. Sarma, G. Desiraju","doi":"10.3109/10559610290252869","DOIUrl":null,"url":null,"abstract":"Thromboxane A(2) receptor antagonists have attracted much attention in recent times in the design of new agents that could be active against diseases such as thrombosis, asthma and myocardial ischemia. 3D-QSAR studies have been performed on a series of [[1-aryl(or benzyl)-1-(benzenesulphonamido)methyl] phenyl] alkanoic acid derivatives by using the receptor surface analysis (RSA) method. The RSA analysis was carried out on 31 analogues of which 25 were used in the training set and the rest considered for the test set. This study produced reasonably good predictive models with good cross-validated and conventional r(2) values in both the models.","PeriodicalId":11297,"journal":{"name":"Drug design and discovery","volume":"65 1","pages":"47-51"},"PeriodicalIF":0.0000,"publicationDate":"2003-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Drug design and discovery","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3109/10559610290252869","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
Thromboxane A(2) receptor antagonists have attracted much attention in recent times in the design of new agents that could be active against diseases such as thrombosis, asthma and myocardial ischemia. 3D-QSAR studies have been performed on a series of [[1-aryl(or benzyl)-1-(benzenesulphonamido)methyl] phenyl] alkanoic acid derivatives by using the receptor surface analysis (RSA) method. The RSA analysis was carried out on 31 analogues of which 25 were used in the training set and the rest considered for the test set. This study produced reasonably good predictive models with good cross-validated and conventional r(2) values in both the models.