DeepProtein: Deep Learning Library and Benchmark for Protein Sequence Learning

Jiaqing Xie, Yue Zhao, Tianfan Fu

Keywords: large molecule learning, protein sequence learning, antibody, benchmark, library, graph neural network, transformer, convolutional neural network

Abstract:

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 is a library that is extended from a well-
known drug discovery library, DeepPurpose. The library is publicly available at
https://anonymous.4open.science/r/DeepProtein-F8FE.

Submission Number: 27