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Poster Teaser
Workshop: Graph Representation Learning and Beyond (GRL+)

(#95 / Sess. 2) Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Protein Structures

Arian Jamasb


Graphein is a python library for constructinggraph and surface-mesh representations ofprotein structures for computational analysis. The library interfaces with popular geometric deep learning libraries: DGL, PyTorch Geometric and PyTorch3D. Geometric deep learning is emerging as a popular methodology in computational structural biology. As feature engineering is a vital step in a machine learning project, the library is designed to be highly flexible, allowing the user to parameterise the graph construction, scalable to facilitate working with large protein complexes, and containing useful pre-processing tools for preparing experimental structure files. Graphein is also designed to facilitate network-based and graph-theoretic analyses of protein structures in a high-throughput manner. As example workflows, we make available two new protein structure-related datasets, previously unused by the geometric deep learning community.

Teaser video |

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