Summary of Evaluating Representation Learning on the Protein Structure Universe, by Arian R. Jamasb and Alex Morehead and Chaitanya K. Joshi and Zuobai Zhang and Kieran Didi and Simon V. Mathis and Charles Harris and Jian Tang and Jianlin Cheng and Pietro Lio and Tom L. Blundell
Evaluating representation learning on the protein structure universe
by Arian R. Jamasb, Alex Morehead, Chaitanya K. Joshi, Zuobai Zhang, Kieran Didi, Simon V. Mathis, Charles Harris, Jian Tang, Jianlin Cheng, Pietro Lio, Tom L. Blundell
First submitted to arxiv on: 19 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Biomolecules (q-bio.BM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces ProteinWorkshop, a benchmark suite for representation learning on protein structures using Geometric Graph Neural Networks (GGNNs). The authors focus on large-scale pre-training and downstream tasks on both experimental and predicted structures to evaluate the quality of learned structural representations. They find that large-scale pre-training improves the performance of GGNNs, particularly for more expressive equivariant models. The goal is to establish a common ground for comparing and advancing protein structure representation learning across machine learning and computational biology communities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ProteinWorkshop is a new tool that helps scientists learn about protein structures using special computer programs called Geometric Graph Neural Networks (GGNNs). These programs help computers understand the shape of proteins, which are important molecules in our bodies. The researchers tested these programs to see how well they work and found that making them learn from lots of data makes them better at understanding protein shapes. |
Keywords
» Artificial intelligence » Machine learning » Representation learning
