Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
{"title":"chempt，一个用于化学空间可视化的Python库","authors":"Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er","doi":"10.1002/cmtd.202200038","DOIUrl":null,"url":null,"abstract":"<p>Invited for this month's cover is the Autonomous Energy Materials Discovery [AMD] Group of Dr. Süleyman Er at DIFFER, and colleagues at CCER and Eindhoven University of Technology (Netherlands). The cover picture shows the ChemPlot-visualized reduced chemical space of molecules enhanced with two-dimensional illustrations of molecules. In addition to being easy-to-use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. Read the full text of their Research Article at 10.1002/cmtd.202200005.</p>","PeriodicalId":72562,"journal":{"name":"Chemistry methods : new approaches to solving problems in chemistry","volume":null,"pages":null},"PeriodicalIF":6.1000,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/cmtd.202200038","citationCount":"0","resultStr":"{\"title\":\"ChemPlot, a Python Library for Chemical Space Visualization\",\"authors\":\"Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er\",\"doi\":\"10.1002/cmtd.202200038\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Invited for this month's cover is the Autonomous Energy Materials Discovery [AMD] Group of Dr. Süleyman Er at DIFFER, and colleagues at CCER and Eindhoven University of Technology (Netherlands). The cover picture shows the ChemPlot-visualized reduced chemical space of molecules enhanced with two-dimensional illustrations of molecules. In addition to being easy-to-use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. Read the full text of their Research Article at 10.1002/cmtd.202200005.</p>\",\"PeriodicalId\":72562,\"journal\":{\"name\":\"Chemistry methods : new approaches to solving problems in chemistry\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":6.1000,\"publicationDate\":\"2022-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://chemistry-europe.onlinelibrary.wiley.com/doi/epdf/10.1002/cmtd.202200038\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Chemistry methods : new approaches to solving problems in chemistry\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cmtd.202200038\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Chemistry methods : new approaches to solving problems in chemistry","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cmtd.202200038","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, MULTIDISCIPLINARY","Score":null,"Total":0}
ChemPlot, a Python Library for Chemical Space Visualization
Invited for this month's cover is the Autonomous Energy Materials Discovery [AMD] Group of Dr. Süleyman Er at DIFFER, and colleagues at CCER and Eindhoven University of Technology (Netherlands). The cover picture shows the ChemPlot-visualized reduced chemical space of molecules enhanced with two-dimensional illustrations of molecules. In addition to being easy-to-use, free and open source, a noteworthy feature of ChemPlot is the application of tailored similarity for the property-sensitive visualization of chemical spaces. ChemPlot streamlines the analysis of molecular datasets by reducing the information to human perception level, tackling the activity/property cliff problem, and facilitating the assessment of the applicability domain of machine learning models in molecular studies. Read the full text of their Research Article at 10.1002/cmtd.202200005.
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
