Papers › DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

2 Oct 2024arXiv:2410.02023archive 2025-07-28

Jiaqing Xie, Tianfan Fu

Deep learning has deeply influenced protein science, enabling breakthroughs in predicting protein properties, higher-order structures, and molecular interactions. This paper introduces DeepProtein, a comprehensive and user-friendly deep learning library tailored for protein-related tasks. It enables researchers to seamlessly address protein data with cutting-edge deep learning models. To assess model performance, we establish a benchmark evaluating different deep learning architectures across multiple protein-related tasks, including protein function prediction, subcellular localization prediction, protein-protein interaction prediction, and protein structure prediction. Furthermore, we introduce DeepProt-T5, a series of fine-tuned Prot-T5-based models that achieve state-of-the-art performance on four benchmark tasks, while demonstrating competitive results on six of others. Comprehensive documentation and tutorials are available which could ensure accessibility and support reproducibility. Built upon the widely used drug discovery library DeepPurpose, DeepProtein is publicly available at https://github.com/jiaqingxie/DeepProtein.

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Deep Learning Drug Discovery Graph Neural Network Prediction Protein Function Prediction Protein Structure Prediction

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Absolute Position Encodings Adam Attention BPE Dense Connections Dropout Graph Neural Network Graph Transformer Label Smoothing LapEigen Laplacian PE Layer Normalization Linear Layer Multi-Head Attention Position-Wise Feed-Forward Layer Residual Connection Softmax Transformer

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