{"title":"A Predictive HQSAR Model for a Series of Tricycle Core Containing MMP-12 Inhibitors with Dibenzofuran Ring.","authors":"Jamal Shamsara, Ahmad Shahir-Sadr","doi":"10.1155/2014/630807","DOIUrl":null,"url":null,"abstract":"<p><p>MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q (2) value of 0.697 and conventional r (2) value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (r pred (2) = 0.8733). Then, the external validity of the model was confirmed by Golbraikh-Tropsha and r m (2) metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors. </p>","PeriodicalId":14082,"journal":{"name":"International Journal of Medicinal Chemistry","volume":"2014 ","pages":"630807"},"PeriodicalIF":0.0000,"publicationDate":"2014-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1155/2014/630807","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medicinal Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1155/2014/630807","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2014/12/7 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
MMP-12 is a member of matrix metalloproteinases (MMPs) family involved in pathogenesis of some inflammatory based diseases. Design of selective matrix MMPs inhibitors is still challenging because of binding pocket similarities among MMPs family. We tried to generate a HQSAR (hologram quantitative structure activity relationship) model for a series of MMP-12 inhibitors. Compounds in the series of inhibitors with reported biological activity against MMP-12 were used to construct a predictive HQSAR model for their inhibitory activity against MMP-12. The HQSAR model had statistically excellent properties and possessed good predictive ability for test set compounds. The HQSAR model was obtained for the 26 training set compounds showing cross-validated q (2) value of 0.697 and conventional r (2) value of 0.986. The model was then externally validated using a test set of 9 compounds and the predicted values were in good agreement with the experimental results (r pred (2) = 0.8733). Then, the external validity of the model was confirmed by Golbraikh-Tropsha and r m (2) metrics. The color code analysis based on the obtained HQSAR model provided useful insights into the structural features of the training set for their bioactivity against MMP-12 and was useful for the design of some new not yet synthesized MMP-12 inhibitors.
期刊介绍:
International Journal of Medicinal Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of chemistry associated with drug discovery, design, and synthesis. International Journal of Medicinal Chemistry is a peer-reviewed, Open Access journal that publishes original research articles as well as review articles in all areas of chemistry associated with drug discovery, design, and synthesis.