Python(编程语言)
可视化
化学空间
计算机科学
空格(标点符号)
封面(代数)
特征(语言学)
数据可视化
人机交互
数据科学
情报检索
相似性(几何)
信息可视化
领域(数学分析)
图形显示
人工智能
特征向量
计算机图形学(图像)
感知
作者
Murat Cihan Sorkun,Dajt Mullaj,J. M. V. A. Koelman,Süleyman Er
标识
DOI:10.1002/cmtd.202200038
摘要
Abstract 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 .
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