Recent advances in interpretable machine learning using structure-based protein representations
Abstract
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.
Cite
@article{arxiv.2409.17726,
title = {Recent advances in interpretable machine learning using structure-based protein representations},
author = {Luiz Felipe Vecchietti and Minji Lee and Begench Hangeldiyev and Hyunkyu Jung and Hahnbeom Park and Tae-Kyun Kim and Meeyoung Cha and Ho Min Kim},
journal= {arXiv preprint arXiv:2409.17726},
year = {2024}
}