ChemPlot, a Python Library for Chemical Space Visualization

IF 6.1 Q1 CHEMISTRY, MULTIDISCIPLINARY Chemistry methods : new approaches to solving problems in chemistry Pub Date : 2022-07-01 DOI:10.1002/cmtd.202200038
Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
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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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chempt,一个用于化学空间可视化的Python库
邀请到本月的封面是自主能源材料发现[AMD]小组,由DIFFER的s leyman Er博士及其在CCER和埃因霍温理工大学(荷兰)的同事组成。封面图显示了chopt可视化的分子化学空间,并增强了分子的二维插图。除了易于使用、免费和开源之外,ChemPlot的一个值得注意的特性是,它为化学空间的属性敏感可视化应用了量身定制的相似性。ChemPlot通过将信息降低到人类感知水平,解决活动/属性悬崖问题,以及促进机器学习模型在分子研究中的适用性评估,简化了分子数据集的分析。阅读他们的研究论文全文:10.1002/cmtd.202200005。
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