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 library is extended from a well-known drug discovery library, DeepPurpose and publicly available at this https URL.
Abstract (translated)
近年来,深度学习已经彻底颠覆了蛋白质科学领域,使预测蛋白质属性、结构折叠和相互作用取得了进展。本文介绍了DeepProtein,一个专门为蛋白质相关任务设计的全面且易用的深度学习库。DeepProtein整合了几个最先进的神经网络架构,包括卷积神经网络(CNN)、循环神经网络(RNN)、Transformer、图神经网络(GNN)和图Transformer(GT)。它提供了易用的界面,使领域研究人员能够将深度学习技术应用于蛋白质数据。我们还策划了一个基准,评估这些神经网络架构在包括蛋白质功能预测、蛋白质定位预测和蛋白质-蛋白质相互作用预测在内的各种蛋白质任务上的性能,展示了其在优越性能和可扩展性方面的优势。此外,我们还提供了详细的文档和教程,以促进其易用性和鼓励可重复的研究。这个库从著名的药物发现库DeepPurpose延伸,并公开发布在https://这个网址。
URL
https://arxiv.org/abs/2410.02023