DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning
<|reference_start|>DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning: In recent years, deep learning has revolutionized the field of protein science, enabling advancements in predicting protein properties, structural folding and interactions. This paper presents DeepProtein, a comprehensive and user-friendly deep learning library specifically designed for protein-related tasks. DeepProtein integrates a couple of state-of-the-art neural network architectures, which include convolutional neural network (CNN), recurrent neural network (RNN), transformer, graph neural network (GNN), and graph transformer (GT). It provides user-friendly interfaces, facilitating domain researchers in applying deep learning techniques to protein data. Also, we curate a benchmark that evaluates these neural architectures on a variety of protein tasks, including protein function prediction, protein localization prediction, and protein-protein interaction prediction, showcasing its superior performance and scalability. Additionally, we provide detailed documentation and tutorials to promote accessibility and encourage reproducible research. This library is extended from a well-known drug discovery library, DeepPurpose and publicly available at https://github.com/jiaqingxie/DeepProtein/tree/main.<|reference_end|>
@article{xie2024deepprotein:,
    title={DeepProtein: Deep Learning Library and Benchmark for Protein Sequence
  Learning},
    author={Jiaqing Xie, Yue Zhao, Tianfan Fu},
    journal={arXiv preprint arXiv:2410.02023},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02023},
    primaryClass={cs.LG cs.AI q-bio.QM}
}