{"title":"药物发现中硅学研究的重要性","authors":"","doi":"10.1016/j.ipha.2024.01.010","DOIUrl":null,"url":null,"abstract":"<div><p>The use of in-silico research in drug development is growing. Aspects of drug discovery and development, such as virtual ligand screening and profiling, target and lead finding, and compound library creation, are simulated by computational approaches. Databases, pharmacophores, homology models, quantitative structure–activity connections, machine learning, data mining, network analysis tools, and computer-based data analysis tools are examples of in-silico techniques. These techniques are mostly applied in conjunction with the production of in vitro data to build models that facilitate the identification and refinement of new compounds by providing insight into their features related to absorption, distribution, metabolism, and excretion.</p></div>","PeriodicalId":100682,"journal":{"name":"Intelligent Pharmacy","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949866X24000200/pdfft?md5=a7867f2e1e88920b4714446ae3598312&pid=1-s2.0-S2949866X24000200-main.pdf","citationCount":"0","resultStr":"{\"title\":\"The importance of in-silico studies in drug discovery\",\"authors\":\"\",\"doi\":\"10.1016/j.ipha.2024.01.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The use of in-silico research in drug development is growing. Aspects of drug discovery and development, such as virtual ligand screening and profiling, target and lead finding, and compound library creation, are simulated by computational approaches. Databases, pharmacophores, homology models, quantitative structure–activity connections, machine learning, data mining, network analysis tools, and computer-based data analysis tools are examples of in-silico techniques. These techniques are mostly applied in conjunction with the production of in vitro data to build models that facilitate the identification and refinement of new compounds by providing insight into their features related to absorption, distribution, metabolism, and excretion.</p></div>\",\"PeriodicalId\":100682,\"journal\":{\"name\":\"Intelligent Pharmacy\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2949866X24000200/pdfft?md5=a7867f2e1e88920b4714446ae3598312&pid=1-s2.0-S2949866X24000200-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Intelligent Pharmacy\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2949866X24000200\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Intelligent Pharmacy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2949866X24000200","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The importance of in-silico studies in drug discovery
The use of in-silico research in drug development is growing. Aspects of drug discovery and development, such as virtual ligand screening and profiling, target and lead finding, and compound library creation, are simulated by computational approaches. Databases, pharmacophores, homology models, quantitative structure–activity connections, machine learning, data mining, network analysis tools, and computer-based data analysis tools are examples of in-silico techniques. These techniques are mostly applied in conjunction with the production of in vitro data to build models that facilitate the identification and refinement of new compounds by providing insight into their features related to absorption, distribution, metabolism, and excretion.