{"title":"Ezqsar:一个直接从结构中开发QSAR模型的R包。","authors":"Jamal Shamsara","doi":"10.2174/1874104501711010212","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.</p><p><strong>Method and materials: </strong>Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.</p><p><strong>Results: </strong>Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.</p><p><strong>Conclusion: </strong>The R package, ezqsar, is freely available <i>via</i> https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.</p>","PeriodicalId":39133,"journal":{"name":"Open Medicinal Chemistry Journal","volume":"11 ","pages":"212-221"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2174/1874104501711010212","citationCount":"12","resultStr":"{\"title\":\"Ezqsar: An R Package for Developing QSAR Models Directly From Structures.\",\"authors\":\"Jamal Shamsara\",\"doi\":\"10.2174/1874104501711010212\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.</p><p><strong>Method and materials: </strong>Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.</p><p><strong>Results: </strong>Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.</p><p><strong>Conclusion: </strong>The R package, ezqsar, is freely available <i>via</i> https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.</p>\",\"PeriodicalId\":39133,\"journal\":{\"name\":\"Open Medicinal Chemistry Journal\",\"volume\":\"11 \",\"pages\":\"212-221\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.2174/1874104501711010212\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Open Medicinal Chemistry Journal\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2174/1874104501711010212\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2017/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"Pharmacology, Toxicology and Pharmaceutics\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Open Medicinal Chemistry Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/1874104501711010212","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2017/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"Pharmacology, Toxicology and Pharmaceutics","Score":null,"Total":0}
Ezqsar: An R Package for Developing QSAR Models Directly From Structures.
Background: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers.
Method and materials: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated.
Results: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example.
Conclusion: The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.