Recent advances in interpretable machine learning using structure-based protein representations
<|reference_start|>Recent advances in interpretable machine learning using structure-based protein representations: Recent advancements in machine learning (ML) are transforming the field of structural biology. For example, AlphaFold, a groundbreaking neural network for protein structure prediction, has been widely adopted by researchers. The availability of easy-to-use interfaces and interpretable outcomes from the neural network architecture, such as the confidence scores used to color the predicted structures, have made AlphaFold accessible even to non-ML experts. In this paper, we present various methods for representing protein 3D structures from low- to high-resolution, and show how interpretable ML methods can support tasks such as predicting protein structures, protein function, and protein-protein interactions. This survey also emphasizes the significance of interpreting and visualizing ML-based inference for structure-based protein representations that enhance interpretability and knowledge discovery. Developing such interpretable approaches promises to further accelerate fields including drug development and protein design.<|reference_end|>
@article{vecchietti2024recent,
    title={Recent advances in interpretable machine learning using structure-based
  protein representations},
    author={Luiz Felipe Vecchietti, Minji Lee, Begench Hangeldiyev, Hyunkyu Jung,
  Hahnbeom Park, Tae-Kyun Kim, Meeyoung Cha, Ho Min Kim},
    journal={arXiv preprint arXiv:2409.17726},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.17726},
    primaryClass={cs.LG}
}