ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Lü, Jing; Carlson, Heather A.

doi:10.1093/bioinformatics/btw523

Cited by 17 publications

(13 citation statements)

References 49 publications

(66 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fig 2 shows a map of the chemical similarity based on ECFP6; the fingerprint was used to give a broader characterization of similarity rather than focusing on one property. The map was generated by ChemTreeMap which was developed in our lab [ 39 ]. Van Westen et al .…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Are there physicochemical differences between allosteric and competitive ligands?

2017

Self Cite

View full text Add to dashboard Cite

Previous studies have compared the physicochemical properties of allosteric compounds to non-allosteric compounds. Those studies have found that allosteric compounds tend to be smaller, more rigid, more hydrophobic, and more drug-like than non-allosteric compounds. However, previous studies have not properly corrected for the fact that some protein targets have much more data than other systems. This generates concern regarding the possible skew that can be introduced by the inherent bias in the available data. Hence, this study aims to determine how robust the previous findings are to the addition of newer data. This study utilizes the Allosteric Database (ASD v3.0) and ChEMBL v20 to systematically obtain large datasets of both allosteric and competitive ligands. This dataset contains 70,219 and 9,511 unique ligands for the allosteric and competitive sets, respectively. Physically relevant compound descriptors were computed to examine the differences in their chemical properties. Particular attention was given to removing redundancy in the data and normalizing across ligand diversity and varied protein targets. The resulting distributions only show that allosteric ligands tend to be more aromatic and rigid and do not confirm the increase in hydrophobicity or difference in drug-likeness. These results are robust across different normalization schemes.

show abstract

Section: Resultsmentioning

confidence: 99%

“…ChemTreeMap [ 52 ] was used to view the chemical space of the structures and display the chemical similarities in the two sets. ChemTreeMap organizes molecules in a hierarchical tree structure to convey molecular similarity information by combining extended connectivity fingerprint and a neighbor-joining algorithm.…”

Section: Methodsmentioning

confidence: 99%

Are there physicochemical differences between allosteric and competitive ligands?

2017

Self Cite

View full text Add to dashboard Cite

show abstract

“…25,26 This limiting behavior has been documented by the ChemTreeMap tool, which can only visualize up to approximately 10,000 data points (molecules or clusters of molecules). 27 Due to the described challenges, large scientific data sets are generally visualized in aggregated or reduced form. 28,29 Here we present an algorithm, named TMAP (Tree MAP), to generate and distribute intuitive visualizations of large data sets in the order of up to 10 7 with arbitrary dimensionality in a tree based on a combination of locality sensitive hashing, graph theory, and modern web technology which also integrates into established data analysis and plotting workflows.…”

Section: Mainmentioning

confidence: 99%

Visualization of very large high-dimensional data sets as minimum spanning trees

2020

View full text Add to dashboard Cite

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 3Dstructures 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 http://tmap.gdb.tools.

show abstract

“…Most of the existing tools to analyze drug screening assays through clustering and visualization of compounds and their corresponding bioactivity measurements have been implemented as stand-alone tools requiring local installation, e.g. Scaffold Hunter ( 15 ), ChemTreeMap ( 16 ), Mona 2 ( 17 ) and Data warrior ( 18 ) or as a browser-based tool, e.g. ChemMineTools ( 19 ).…”

Section: Introductionmentioning

confidence: 99%

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms

Ravikumar

Alam

Gopalacharyulu

et al. 2017

Nucleic Acids Research

View full text Add to dashboard Cite

The advent of polypharmacology paradigm in drug discovery calls for novel chemoinformatic tools for analyzing compounds’ multi-targeting activities. Such tools should provide an intuitive representation of the chemical space through capturing and visualizing underlying patterns of compound similarities linked to their polypharmacological effects. Most of the existing compound-centric chemoinformatics tools lack interactive options and user interfaces that are critical for the real-time needs of chemical biologists carrying out compound screening experiments. Toward that end, we introduce C-SPADE, an open-source exploratory web-tool for interactive analysis and visualization of drug profiling assays (biochemical, cell-based or cell-free) using compound-centric similarity clustering. C-SPADE allows the users to visually map the chemical diversity of a screening panel, explore investigational compounds in terms of their similarity to the screening panel, perform polypharmacological analyses and guide drug-target interaction predictions. C-SPADE requires only the raw drug profiling data as input, and it automatically retrieves the structural information and constructs the compound clusters in real-time, thereby reducing the time required for manual analysis in drug development or repurposing applications. The web-tool provides a customizable visual workspace that can either be downloaded as figure or Newick tree file or shared as a hyperlink with other users. C-SPADE is freely available at http://cspade.fimm.fi/.

show abstract

ChemTreeMap: an interactive map of biochemical similarity in molecular datasets

Cited by 17 publications

References 49 publications

Are there physicochemical differences between allosteric and competitive ligands?

Are there physicochemical differences between allosteric and competitive ligands?

Visualization of very large high-dimensional data sets as minimum spanning trees

C-SPADE: a web-tool for interactive analysis and visualization of drug screening experiments through compound-specific bioactivity dendrograms

Contact Info

Product

Resources

About