Comprehensive applications of the artificial intelligence technology in new drug research and development.

IF 4.7 3区医学 Q1 MEDICAL INFORMATICS

Health Information Science and Systems Pub Date : 2024-08-08 eCollection Date: 2024-12-01 DOI:10.1007/s13755-024-00300-y

Hongyu Chen, Dong Lu, Ziyi Xiao, Shensuo Li, Wen Zhang, Xin Luan, Weidong Zhang, Guangyong Zheng

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引用次数: 0

Abstract

Purpose: Target-based strategy is a prevalent means of drug research and development (R&D), since targets provide effector molecules of drug action and offer the foundation of pharmacological investigation. Recently, the artificial intelligence (AI) technology has been utilized in various stages of drug R&D, where AI-assisted experimental methods show higher efficiency than sole experimental ones. It is a critical need to give a comprehensive review of AI applications in drug R &D for biopharmaceutical field.

Methods: Relevant literatures about AI-assisted drug R&D were collected from the public databases (Including Google Scholar, Web of Science, PubMed, IEEE Xplore Digital Library, Springer, and ScienceDirect) through a keyword searching strategy with the following terms [("Artificial Intelligence" OR "Knowledge Graph" OR "Machine Learning") AND ("Drug Target Identification" OR "New Drug Development")].

Results: In this review, we first introduced common strategies and novel trends of drug R&D, followed by characteristic description of AI algorithms widely used in drug R&D. Subsequently, we depicted detailed applications of AI algorithms in target identification, lead compound identification and optimization, drug repurposing, and drug analytical platform construction. Finally, we discussed the challenges and prospects of AI-assisted methods for drug discovery.

Conclusion: Collectively, this review provides comprehensive overview of AI applications in drug R&D and presents future perspectives for biopharmaceutical field, which may promote the development of drug industry.

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人工智能技术在新药研发中的全面应用。

目的：基于靶点的策略是药物研发（R&D）的普遍手段，因为靶点提供了药物作用的效应分子，为药理学研究提供了基础。最近，人工智能（AI）技术被应用于药物研发的各个阶段，其中人工智能辅助实验方法比单独实验方法显示出更高的效率。因此，亟需对人工智能在生物制药领域药物研发中的应用进行全面综述：方法：通过关键词检索策略，以[（"人工智能 "或 "知识图谱 "或 "机器学习"）和（"药物靶点识别 "或 "新药研发"）]为关键词，从公共数据库（包括 Google Scholar、Web of Science、PubMed、IEEE Xplore Digital Library、Springer 和 ScienceDirect）中收集有关人工智能辅助药物研发的相关文献：在这篇综述中，我们首先介绍了药物研发的常见策略和新趋势，然后介绍了广泛应用于药物研发的人工智能算法的特点。随后，我们详细介绍了人工智能算法在靶点识别、先导化合物识别与优化、药物再利用以及药物分析平台构建等方面的应用。最后，我们讨论了人工智能辅助药物发现方法所面临的挑战和前景：综上所述，本综述全面概述了人工智能在药物研发中的应用，并提出了生物制药领域的未来展望，可促进药物产业的发展。

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

Health Information Science and Systems MEDICAL INFORMATICS-

CiteScore

11.30

自引率

5.00%

发文量

期刊介绍： Health Information Science and Systems is a multidisciplinary journal that integrates artificial intelligence/computer science/information technology with health science and services, embracing information science research coupled with topics related to the modeling, design, development, integration and management of health information systems, smart health, artificial intelligence in medicine, and computer aided diagnosis, medical expert systems. The scope includes: i.) smart health, artificial Intelligence in medicine, computer aided diagnosis, medical image processing, medical expert systems ii.) medical big data, medical/health/biomedicine information resources such as patient medical records, devices and equipments, software and tools to capture, store, retrieve, process, analyze, optimize the use of information in the health domain, iii.) data management, data mining, and knowledge discovery, all of which play a key role in decision making, management of public health, examination of standards, privacy and security issues, iv.) development of new architectures and applications for health information systems.