{"title":"基于定量构效关系(QSAR)的非酸性润滑剂预测模型的建立","authors":"D. Barr, Christopher L. Friend","doi":"10.4271/2005-01-3900","DOIUrl":null,"url":null,"abstract":"This study describes the use of Quantitative Structure Activity Relationships (QSAR) to develop predictive models for non-acidic Lubricity agents. The work demonstrates the importance of separating certain chemical families to give better and more robust equations rather than grouping a whole data set together. These models can then be used as important tools in further development work by predicting activities of new compounds before actual synthesis/testing.","PeriodicalId":21404,"journal":{"name":"SAE transactions","volume":"58 1","pages":"1845-1856"},"PeriodicalIF":0.0000,"publicationDate":"2005-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"The Development of Predictive Models for Non-Acidic Lubricity Agents (NALA) using Quantitative Structure Activity Relationships (QSAR)\",\"authors\":\"D. Barr, Christopher L. Friend\",\"doi\":\"10.4271/2005-01-3900\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This study describes the use of Quantitative Structure Activity Relationships (QSAR) to develop predictive models for non-acidic Lubricity agents. The work demonstrates the importance of separating certain chemical families to give better and more robust equations rather than grouping a whole data set together. These models can then be used as important tools in further development work by predicting activities of new compounds before actual synthesis/testing.\",\"PeriodicalId\":21404,\"journal\":{\"name\":\"SAE transactions\",\"volume\":\"58 1\",\"pages\":\"1845-1856\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2005-10-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE transactions\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/2005-01-3900\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE transactions","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/2005-01-3900","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The Development of Predictive Models for Non-Acidic Lubricity Agents (NALA) using Quantitative Structure Activity Relationships (QSAR)
This study describes the use of Quantitative Structure Activity Relationships (QSAR) to develop predictive models for non-acidic Lubricity agents. The work demonstrates the importance of separating certain chemical families to give better and more robust equations rather than grouping a whole data set together. These models can then be used as important tools in further development work by predicting activities of new compounds before actual synthesis/testing.