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:
| Hardware | Number of Cores used | Rounded Run Time |
|---|---|---|
| 12th generation Intel Core i9, 64 GB RAM | 22 | 25 min |
| Raspberry Pi 5, 8 GB RAM | 3 | 3 hours |
| Raspberry Pi 400, 4 GB RAM | 3 | 5 hours |