Asim Muhammad Nabeel, Asif Tayyaba, Mehmood Faiza, Dengel Andreas
German Research Center for Artificial Intelligence, Kaiserslautern, 67663, Germany; Intelligentx GmbH (intelligentx.com), Kaiserslautern, Germany.
Department of Computer Science, Rhineland-Palatinate Technical University of Kaiserslautern-Landau, Kaiserslautern, 67663, Germany.
Comput Biol Med. 2025 Apr;188:109821. doi: 10.1016/j.compbiomed.2025.109821. Epub 2025 Feb 22.
Peptides are gaining significant attention in diverse fields such as the pharmaceutical market has seen a steady rise in peptide-based therapeutics over the past six decades. Peptides have been utilized in the development of distinct applications including inhibitors of SARS-COV-2 and treatments for conditions like cancer and diabetes. Distinct types of peptides possess unique characteristics, and development of peptide-specific applications require the discrimination of one peptide type from others. To the best of our knowledge, approximately 230 Artificial Intelligence (AI) driven applications have been developed for 22 distinct types of peptides, yet there remains significant room for development of new predictors. A Comprehensive review addresses the critical gap by providing a consolidated platform for the development of AI-driven peptide classification applications. This paper offers several key contributions, including presenting the biological foundations of 22 unique peptide types and categorizes them into four main classes: Regulatory, Therapeutic, Nutritional, and Delivery Peptides. It offers an in-depth overview of 47 databases that have been used to develop peptide classification benchmark datasets. It summarizes details of 288 benchmark datasets that are used in development of diverse types AI-driven peptide classification applications. It provides a detailed summary of 197 sequence representation learning methods and 94 classifiers that have been used to develop 230 distinct AI-driven peptide classification applications. Across 22 distinct types peptide classification tasks related to 288 benchmark datasets, it demonstrates performance values of 230 AI-driven peptide classification applications. It summarizes experimental settings and various evaluation measures that have been employed to assess the performance of AI-driven peptide classification applications. The primary focus of this manuscript is to consolidate scattered information into a single comprehensive platform. This resource will greatly assist researchers who are interested in developing new AI-driven peptide classification applications.
肽在各个领域正受到广泛关注，例如在过去六十年中，基于肽的疗法在制药市场稳步增长。肽已被用于开发多种不同的应用，包括新冠病毒抑制剂以及癌症和糖尿病等疾病的治疗方法。不同类型的肽具有独特的特性，开发针对特定肽的应用需要区分不同类型的肽。据我们所知，已经针对22种不同类型的肽开发了约230种人工智能驱动的应用，但新预测器的开发仍有很大空间。一项全面综述通过提供一个整合平台来开发人工智能驱动的肽分类应用，填补了这一关键空白。本文做出了几项关键贡献，包括介绍22种独特肽类型的生物学基础，并将它们分为四个主要类别：调节肽、治疗肽、营养肽和递送肽。它深入概述了用于开发肽分类基准数据集的47个数据库。它总结了用于开发各种类型人工智能驱动的肽分类应用的288个基准数据集的详细信息。它详细总结了用于开发230种不同的人工智能驱动的肽分类应用的197种序列表示学习方法和94种分类器。在与288个基准数据集相关的22种不同类型的肽分类任务中，它展示了230种人工智能驱动的肽分类应用的性能值。它总结了用于评估人工智能驱动的肽分类应用性能的实验设置和各种评估措施。本手稿的主要重点是将分散的信息整合到一个单一的综合平台中。这一资源将极大地帮助有兴趣开发新的人工智能驱动的肽分类应用的研究人员。
