Murat Cihan Sorkun, Dajt Mullaj, J. M. Vianney A. Koelman, Süleyman Er
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引用次数: 0
摘要
前封面显示了chopt可视化的分子化学空间，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。更多信息可以在Murat C. Sorkun等人的研究文章中找到。
Cover Picture: ChemPlot, a Python Library for Chemical Space Visualization (Chem. Methods 7/2022)
The Front Cover 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. More information can be found in the Research Article by Murat C. Sorkun et al.
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