@article{8814,
  author = {M. C. Sorkun and D. Mullaj and J. M. V. A. Koelman and S. Er},
  title = {ChemPlot, a Python Library for Chemical Space Visualization},
  abstract = {Visualizing chemical spaces streamlines the analysis of molecular datasets by reducing the information to human perception level, hence it forms an integral piece of molecular engineering, including chemical library design, high-throughput screening, diversity analysis, and outlier detection. We present here ChemPlot, which enables users to visualize the chemical space of molecular datasets in both static and interactive ways. ChemPlot features structural and tailored similarity methods, together with three different dimensionality reduction methods: PCA, t-SNE, and UMAP. ChemPlot is the first visualization software that tackles the activity/property cliff problem by incorporating tailored similarity. With tailored similarity, the chemical space is constructed in a supervised manner considering target properties. Additionally, we propose a metric, the Distance Property Relationship score, to quantify the property difference of similar (i. e. close) molecules in the visualized chemical space. ChemPlot can be installed via Conda or PyPI (pip) and a web application is freely accessible at https://www.amdlab.nl/chemplot/.

 
},
  year = {2022},
  journal = {Chemistry-Methods},
  volume = {2},
  pages = {e202200005},
  doi = {10.1002/cmtd.202200039},
  language = {eng},
}