Summary of Recent Advances in Interpretable Machine Learning Using Structure-based Protein Representations, by Luiz Felipe Vecchietti et al.
Recent advances in interpretable machine learning using structure-based protein representations
by Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Hyunkyu Jung, Hahnbeom Park, Tae-Kyun Kim, Meeyoung Cha, Ho Min Kim
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Machine learning is revolutionizing structural biology, with AlphaFold’s neural network for protein structure prediction being widely adopted by researchers. This paper presents various methods for representing protein 3D structures from low- to high-resolution, showcasing how interpretable ML methods support tasks such as predicting protein structures, functions, and interactions. By emphasizing the importance of interpreting and visualizing ML-based inference, this survey aims to accelerate fields like drug development and protein design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using machine learning to help scientists understand protein structures, which are really important for developing new medicines and designing new proteins. It’s like a super powerful tool that can predict how proteins will look and what they do. The researchers are also trying to make it easier for other scientists to use this tool by making the results easy to understand and visualize. |
Keywords
» Artificial intelligence » Inference » Machine learning » Neural network
