This repository contains the Supporting Information code for:

Scalfani, V.F., Patel, V.D. & Fernandez, A.M. Visualizing chemical space networks with RDKit and NetworkX. J Cheminform, 2022, 14, 87. https://doi.org/10.1186/s13321-022-00664-x

@article{scalfani2022visualizing,
  title={Visualizing chemical space networks with RDKit and NetworkX},
  author={Scalfani, Vincent F and Patel, Vishank D and Fernandez, Avery M},
  journal={Journal of Cheminformatics},
  volume={14},
  number={1},
  pages={87},
  year={2022},
  publisher={Springer}
}

The original Jupyter Notebooks associated with the manuscript are in the CSN_Jupyter_Notebooks/ folder. The glucocorticoid_recepter_2034_2.csv ChEMBL dataset (Additional File 1 in manuscript) is also provided in the Dataset/ folder. Please read the dataset_license file for the dataset reuse terms.

The Less_Memory_Calculations/ folder contains an alternative script for the CSN calculations that uses less memory. This script/method was not part of the original article; we added it here as it was useful to us for running the calculations on a Raspberry Pi 400 with only 4 GB RAM.

Approximate run times for the CSN_calculations_lessMem.py script: