ProteinShake: Building datasets and benchmarks for deep learning on protein structures
Tim Kucera, Carlos Oliver, Dexiong Chen, Karsten Borgwardt
Abstract
We present ProteinShake, a Python software package that simplifies dataset creation and model evaluation for deep learning on protein structures. Users can create custom datasets or load an extensive set of pre-processed datasets from biological data repositories such as the Protein Data Bank (PDB) and AlphaFoldDB. Each dataset is associated with prediction tasks and evaluation functions covering a broad array of biological challenges. A benchmark on these tasks shows that pre- training almost always improves performance, the optimal data modality (graphs, voxel grids, or point clouds) is task-dependent, and models struggle to generalize to new structures. ProteinShake makes protein structure data easily accessible and comparison among models straightforward, providing challenging benchmark settings with real-world implications. ProteinShake is available at: https://proteinshake.ai
BibTeX
@inproceedings{
kucera2023proteinshake,
title={ProteinShake: Building datasets and benchmarks for deep learning on protein structures},
author={Tim Kucera and Carlos Oliver and Dexiong Chen and Karsten Borgwardt},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2023},
url={https://openreview.net/forum?id=27vPcG4vKV}
}