graphein and dgl
Graphein is a specialized protein structure featurization tool that can output graph representations compatible with DGL, making them complements rather than competitors—you'd use Graphein to prepare biological data and DGL to train neural networks on the resulting graphs.
About graphein
a-r-j/graphein
Protein Graph Library
This project helps computational biologists and biochemists analyze the intricate structures of proteins and RNA. It takes protein data from sources like the PDB or AlphaFold2 predictions, or RNA from dotbracket notation, and converts them into structured graph representations. These graphs can then be used to study interactions, predict functions, or explore structural properties.
About dgl
dmlc/dgl
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Deep Graph Library (DGL) helps researchers and practitioners apply deep learning to data structured as graphs. It takes graph data, which can represent anything from social networks to molecular structures, and helps you build machine learning models to make predictions or find patterns within them. This is for anyone working with interconnected data who wants to leverage advanced AI techniques.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work