{"title":"30种活性化合物抑制乙酰胆碱酯酶的QSAR模型","authors":"N. Hammoudi, Yacine Benguerba, W. Sobhi","doi":"10.3844/ajbsp.2019.62.65","DOIUrl":null,"url":null,"abstract":"This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.","PeriodicalId":11025,"journal":{"name":"Current Research in Bioinformatics","volume":"27 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme\",\"authors\":\"N. Hammoudi, Yacine Benguerba, W. Sobhi\",\"doi\":\"10.3844/ajbsp.2019.62.65\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.\",\"PeriodicalId\":11025,\"journal\":{\"name\":\"Current Research in Bioinformatics\",\"volume\":\"27 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current Research in Bioinformatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3844/ajbsp.2019.62.65\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Research in Bioinformatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3844/ajbsp.2019.62.65","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
QSAR Modeling of Thirty Active Compounds for the Inhibition of the Acetylcholinesterase Enzyme
This work aims at developing a reliable and predictive QSAR model which allows, on one hand, an exploration of the main molecular descriptors responsible for the inhibitory activity towards the Acetylcholinesterase enzyme and, on the other hand, predict the inhibitory activity of new compounds before testing them experimentally. This study involves a series of DL0410 and its 29 DL0410 derivatives. The Multiple Linear Regression (MLR) analysis is carried out to derive the QSAR model. The results indicate that the QSAR model is robust and possesses a high predictive capacity.