P. Ganga Raju Achary , Sanija Begum , Alla P. Toropova , Andrey A. Toropov
{"title":"A quasi-SMILES based QSPR Approach towards the prediction of adsorption energy of Ziegler − Natta catalysts for propylene polymerization","authors":"P. Ganga Raju Achary , Sanija Begum , Alla P. Toropova , Andrey A. Toropov","doi":"10.1016/j.md.2016.12.003","DOIUrl":null,"url":null,"abstract":"<div><p>QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.</p></div>","PeriodicalId":100888,"journal":{"name":"Materials Discovery","volume":"5 ","pages":"Pages 22-28"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1016/j.md.2016.12.003","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Materials Discovery","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352924516300370","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
Abstract
QSPR models are well known for reliable prediction of physicochemical properties. The quasi-SMILES code of the internal donor molecules are derived from the different molecular fragments of phthalates, 1,3-diethers and malonates. The adsorption energy has been modeled with the optimal descriptor derived from such quasi-SMILES codes. QSPR model was successfully applied for designing 24 new internal donors for the ZN catalyzed propylene polymerization with better adsorption energies. The science of quasi-QSPR model is not sufficient for an abstract, therefore described in detail in the paper so that one can learn how to develop the quasi-SMILES based models.