pytorch_geometric and graphein
PyTorch Geometric provides the foundational GNN framework and operations, while Graphein builds specialized protein structure graphs on top of it, making them complements that are often used together in computational biology workflows.
About pytorch_geometric
pyg-team/pytorch_geometric
Graph Neural Network Library for PyTorch
This tool helps machine learning engineers and researchers build and train Graph Neural Networks (GNNs) for analyzing structured data. It takes graph-structured data (like social networks, molecular structures, or citation graphs) as input and produces trained GNN models capable of tasks such as classifying nodes, predicting links, or generating new graphs. It is designed for those already familiar with PyTorch.
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.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work