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Networks of Topologically Linked Chemical Inhibitors in Mathematica

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Chemical Space Networks in Mathematica

A repository that holds code for an implementation of Chemical Space Networks in Mathematica. For more information see Maggiora, G.M., Bajorath, J. Chemical space networks: a powerful new paradigm for the description of chemical space. J Comput Aided Mol Des 28, 795–802 (2014) as well as my post on wolfram community.

Instructions

ChemicalSpaceNetwork - The Function

The recommended way to use the ChemicalSpaceNetwork function is through the Wolfram Function repository by calling

ResourceFunction["ChemicalSpaceNetwork"]

If you want to run the code locally, download or clone the repository. The file structure includes

├── src
│   ├── nb
│   │   ├── notebook files
│   ├── src
│   │   ├── *.wl files
├── Data
│   ├── sample datasets

The notebook "chemicalSpaceNetwork.nb" holds the same data as "chemicalSpaceNetwork.wl" just in notebook format, run either file and the chemicalSpaceNetwork endpoint will be exposed.

GUI Instructions

If you want to access the UI within Mathematica, first run the function ChemicalSpaceNetwork and ensure that the option "UI" -> True. Then download and run the code contained in the userInterface.nb file.

Warning The chemicalSpaceNetwork.nb file is not cross compatible with the user interface, in order to use the UI you should use either the resource function or package script chemicalSpaceNetwork.wl

Sample Datasets

Included under the Data folder are three sample datasets for three targets. The data was collected from the ChEMBL database using the same workflow described in Scalfani, V.F., Patel, V.D. & Fernandez, A.M. Visualizing chemical space networks with RDKit and NetworkX. J Cheminform 14, 87 (2022).

The dataset includes the one used in Scalfani as well as two additional targets, the D5 dopamine receptor and the cyclin dependent kinase protein family.

Documentation

For further documentation and examples see this post as well as the function repository submission.

Acknowledgements

  • This project would not have been possible without contributions from the Wolfram Research community, including help from my mentor Robert Nachbar, as well as Stephen Wolfram.

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