 Here, we introduce a new data visualization and exploration method, TMAP (tree-map), which exploits locality sensitive hashing, Kruskal's minimum-spanning-tree algorithm, and a multilevel multipole-based graph layout algorithm to represent large and high dimensional data sets as a tree structure, which is readily understandable and explorable. Compared to other data visualization methods such as t-SNE or UMAP , TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data. We exemplify TMAP in the area of cheminformatics with interactive maps for 1.16 million drug-like molecules from ChEMBL, 10.1 million small molecule fragments from FDB17,  and 131 thousand 3D- structures of biomolecules from the PDB Databank, and to visualize data from literature (GUTENBERG data set), cancer biology(PANSCAN data set) and particle physics(MiniBooNE data set). TMAP is available as a Python package. Installation, usage instructions and application examples can be found at URL
The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation. Here, we propose a solution to this problem with a new data visualization method, TMAP , capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree URL Visualizations based on TMAP are better suited than t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, the Natural Products Atlas, DSSTox, DrugBank, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics, and literature.
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The chemical sciences are producing an unprecedented amount of large, high-dimensional data sets containing chemical structures and associated properties. However, there are currently no algorithms to visualize such data while preserving both global and local features with a sufficient level of detail to allow for human inspection and interpretation.
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(tree-map), which exploits locality sensitive hashing, Kruskal's minimum-spanning-tree algorithm, and a multilevel multipole-based graph layout algorithm to represent large and high dimensional data sets as a tree structure, which is readily understandable and explorable. Compared to other data visualization methods such as
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, TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data. We exemplify TMAP in the area of cheminformatics with interactive maps for 1.16 million drug-like molecules from ChEMBL, 10.1 million small molecule fragments from
for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL,
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the Natural Products Atlas, DSSTox, DrugBank, as well as to the MoleculeNet benchmark collection of data sets. We also show its broad applicability with further examples from biology, particle physics,
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131 thousand 3D- structures of biomolecules from the PDB Databank, and to visualize data from literature (GUTENBERG data set), cancer biology(PANSCAN data set) and particle physics(MiniBooNE data set). TMAP is available as a Python package. Installation, usage instructions and application examples can be found at URL
literature.