Over the past few years, deep learning has demonstrated itself to be a powerful tool in many areas, especially bioinformatics. With its previous success in DNA and protein related studies, deep learning has now been brought to the field of peptide science as well. It has been widely used in sequence-based peptide identification, generation, and property prediction. The publications on this subject over the past two years are summarized in this review. The deep learning models reported are mainly convolutional neural networks, recurrent neural networks, hybrid models, transformers, and other generative models like variational autoencoders and generative adversarial networks, as well as algorithms like input optimization. Application areas include antimicrobial peptides, signal peptides, and major histocompatibility complex binding peptides, among others. This review develops content according to the general workflow of deep learning, while illustrating adaptations and techniques specific to certain example problems. Some issues and future directions are also discussed, such as approaches for model interpretation, benchmark datasets, automation in deep learning, and rational peptide design techniques.

中文翻译：

---

深度学习基于序列的肽鉴定，生成和特性预测：综述

在过去的几年中，深度学习已经证明自己是许多领域的强大工具，尤其是生物信息学。凭借先前在DNA和蛋白质相关研究中的成功，深度学习现在也已被带入肽科学领域。它已广泛用于基于序列的肽鉴定，生成和特性预测。这篇综述总结了过去两年中有关该主题的出版物。报告的深度学习模型主要是卷积神经网络，递归神经网络，混合模型，转换器和其他生成模型（例如变分自动编码器和生成对抗网络）以及算法（例如输入优化）。应用领域包括抗菌肽，信号肽和主要的组织相容性复合物结合肽，其中。这篇评论根据深度学习的一般工作流程开发了内容，同时说明了特定于某些示例问题的改编和技术。还讨论了一些问题和未来方向，例如模型解释方法，基准数据集，深度学习自动化和合理的肽设计技术。