A Python library for visualizing dynamically generated protein interactions using 3D node-link layout.
-
Clone the github repository
-
Requires
anaconda
andpython=3.8
conda create -n myenv python=3.8 conda activate myenv
-
Install packages
cd src/ pip install -r requirements.txt
Note: You may need to install pygraphvis using conda forge:
conda install --channel conda-forge pygraphviz
-
The data file in dot file format should be stored in
./src/visg/static/data/
-
Start server
./start.sh
-
Open browser (Google Chrome preferred)
http://127.0.0.1:5000/index
-
Change directory to
./src
cd /path/to/project/src
-
Activate conda environment
conda activate myenv
-
Start server
./start.sh
-
Open browser (Google Chrome preferred)
http://127.0.0.1:5000/index
-
A. Vasan et al., "High Performance Binding Affinity Prediction with a Transformer-Based Surrogate Model," 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), San Francisco, CA, USA, 2024, pp. 571-580, doi: 10.1109/IPDPSW63119.2024.00114. keywords: {Proteins;Ion radiation effects;Accuracy;Computational modeling;Pipelines;Transformers;Supercomputers;drug discovery;virtual screening;docking surrogates;high performance computing;transformers},
-
Libraries used: D3, Flask, 3d-force-graph