{"title":"利用Levenberg-Marquardt神经网络模拟香豆素抗白色念珠菌生物活性的热力学和理化性质。","authors":"Seyyedeh Soghra Mousavi, Hanieh Bokharaie, Shadi Rahimi, Sima Azadi Soror, Mehrdad Hamidi","doi":"10.2147/aabc.s11812","DOIUrl":null,"url":null,"abstract":"<p><p>In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg-Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.</p>","PeriodicalId":53584,"journal":{"name":"Advances and Applications in Bioinformatics and Chemistry","volume":"3 ","pages":"59-66"},"PeriodicalIF":0.0000,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2147/aabc.s11812","citationCount":"9","resultStr":"{\"title\":\"Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg-Marquardt neural network.\",\"authors\":\"Seyyedeh Soghra Mousavi, Hanieh Bokharaie, Shadi Rahimi, Sima Azadi Soror, Mehrdad Hamidi\",\"doi\":\"10.2147/aabc.s11812\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg-Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.</p>\",\"PeriodicalId\":53584,\"journal\":{\"name\":\"Advances and Applications in Bioinformatics and Chemistry\",\"volume\":\"3 \",\"pages\":\"59-66\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.2147/aabc.s11812\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances and Applications in Bioinformatics and Chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2147/aabc.s11812\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2010/8/13 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q2\",\"JCRName\":\"Biochemistry, Genetics and Molecular Biology\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances and Applications in Bioinformatics and Chemistry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2147/aabc.s11812","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2010/8/13 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"Biochemistry, Genetics and Molecular Biology","Score":null,"Total":0}
Modeling of thermodynamic and physico-chemical properties of coumarins bioactivity against Candida albicans using a Levenberg-Marquardt neural network.
In recent years, due to vital need for novel fungicidal agents, investigation on natural antifungal resources has been increased. The special features exhibited by neural network classifiers make them suitable for handling complex problems like analyzing different properties of candidate compounds in computer-aided drug design. In this study, by using a Levenberg-Marquardt (LM) neural network (the fastest of the training algorithms), the relation between some important thermodynamic and physico-chemical properties of coumarin compounds and their biological activities (tested against Candida albicans) has been evaluated. A set of already reported antifungal bioactive coumarin and some well-known physical descriptors have been selected and using LM training algorithm the best architecture of neural model has been designed for forecasting the new bioactive compounds.