A Comprehensive Survey of Studies on Predicting Anatomical Therapeutic Chemical Classes of Drugs

IF 23.8 1区计算机科学 Q1 COMPUTER SCIENCE, THEORY & METHODS

ACM Computing Surveys Pub Date : 2024-10-08 DOI:10.1145/3699713

Pranab Das, Dilwar Hussain Mazumder

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Abstract

Drug classification plays a crucial role in contemporary drug discovery, design, and development. Determining the Anatomical Therapeutic Chemical (ATC) classes for new drugs is a laborious, costly, and intricate process, often requiring multiple clinical trial phases. Computational models offer significant benefits by accelerating drug evaluation, reducing complexity, and lowering costs; however, challenges persist in the drug classification system. To address this, a literature survey of computational models used for predicting ATC classes was conducted, covering research from 2008 to 2024. This study reviews numerous research articles on drug classification, focusing on drug descriptors, data sources, tasks, computational methods, model performance, and challenges in predicting ATC classes. It also examines the evolution of computational techniques and their application in identifying ATC classes. Finally, the study highlights open problems and research gaps, suggesting areas for further investigation in ATC class prediction. CCS Concepts: Applied computing → Life and medical sciences → Bioinformatics

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预测药物解剖治疗化学类别研究的综合调查

药物分类在当代药物发现、设计和开发中起着至关重要的作用。确定新药的解剖治疗化学（ATC）类别是一个费力、昂贵且复杂的过程，通常需要多个临床试验阶段。计算模型通过加速药物评估、减少复杂性和降低成本带来了显著的好处；然而，药物分类系统仍然面临挑战。为了解决这个问题，我们对用于预测 ATC 类别的计算模型进行了文献调查，调查范围涵盖 2008 年至 2024 年的研究。本研究回顾了有关药物分类的大量研究文章，重点关注药物描述符、数据源、任务、计算方法、模型性能以及预测 ATC 类别所面临的挑战。研究还探讨了计算技术的发展及其在确定 ATC 类别中的应用。最后，研究强调了尚未解决的问题和研究空白，提出了在 ATC 类别预测方面需要进一步研究的领域。CCS 概念：应用计算 → 生命和医学科学 → 生物信息学

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACM Computing Surveys 工程技术-计算机：理论方法

CiteScore

33.20

自引率

0.60%

发文量

372

审稿时长

12 months

期刊介绍： ACM Computing Surveys is an academic journal that focuses on publishing surveys and tutorials on various areas of computing research and practice. The journal aims to provide comprehensive and easily understandable articles that guide readers through the literature and help them understand topics outside their specialties. In terms of impact, CSUR has a high reputation with a 2022 Impact Factor of 16.6. It is ranked 3rd out of 111 journals in the field of Computer Science Theory & Methods. ACM Computing Surveys is indexed and abstracted in various services, including AI2 Semantic Scholar, Baidu, Clarivate/ISI: JCR, CNKI, DeepDyve, DTU, EBSCO: EDS/HOST, and IET Inspec, among others.