人工智能在药物配方与开发中的应用与前景。

IF 2.1 4区 医学 Q4 BIOCHEMISTRY & MOLECULAR BIOLOGY Current drug metabolism Pub Date : 2023-01-01 DOI:10.2174/0113892002265786230921062205
Noorain, Varsha Srivastava, Bushra Parveen, Rabea Parveen
{"title":"人工智能在药物配方与开发中的应用与前景。","authors":"Noorain, Varsha Srivastava, Bushra Parveen, Rabea Parveen","doi":"10.2174/0113892002265786230921062205","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.</p>","PeriodicalId":10770,"journal":{"name":"Current drug metabolism","volume":" ","pages":"622-634"},"PeriodicalIF":2.1000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.\",\"authors\":\"Noorain, Varsha Srivastava, Bushra Parveen, Rabea Parveen\",\"doi\":\"10.2174/0113892002265786230921062205\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.</p>\",\"PeriodicalId\":10770,\"journal\":{\"name\":\"Current drug metabolism\",\"volume\":\" \",\"pages\":\"622-634\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Current drug metabolism\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2174/0113892002265786230921062205\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"BIOCHEMISTRY & MOLECULAR BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current drug metabolism","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2174/0113892002265786230921062205","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0

摘要

人工智能已经成为各个领域的强大工具,药物配方和开发领域也不例外。这篇综述文章旨在概述人工智能在药物配方和开发中的应用,并探讨其未来前景。文章首先介绍了人工智能的基本概念,包括机器学习、深度学习和人工神经网络及其在制药行业中的相关性。此外,文章还讨论了人工智能的网络和工具及其在药物开发过程中的应用,包括药物发现、制造、质量控制、临床试验管理和药物交付等各个领域。人工智能在各种传统剂型和改良剂型中的应用已经汇编完毕。它还强调了人工智能在该领域实施的挑战和局限性,包括数据可用性、模型可解释性和监管考虑因素。最后,文章展望了人工智能在药物配方和开发中的未来前景,强调了其在个性化用药、精准药物靶向和快速配方优化方面的潜力。它还讨论了人工智能在这一背景下的伦理含义,包括隐私、偏见和问责制问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

Artificial Intelligence (AI) has emerged as a powerful tool in various domains, and the field of drug formulation and development is no exception. This review article aims to provide an overview of the applications of AI in drug formulation and development and explore its future prospects. The article begins by introducing the fundamental concepts of AI, including machine learning, deep learning, and artificial neural networks and their relevance in the pharmaceutical industry. Furthermore, the article discusses the network and tools of AI and its applications in the pharmaceutical development process, including various areas, such as drug discovery, manufacturing, quality control, clinical trial management, and drug delivery. The utilization of AI in various conventional as well as modified dosage forms has been compiled. It also highlights the challenges and limitations associated with the implementation of AI in this field, including data availability, model interpretability, and regulatory considerations. Finally, the article presents the future prospects of AI in drug formulation and development, emphasizing the potential for personalized medicine, precision drug targeting, and rapid formulation optimization. It also discusses the ethical implications of AI in this context, including issues of privacy, bias, and accountability.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Current drug metabolism
Current drug metabolism 医学-生化与分子生物学
CiteScore
4.30
自引率
4.30%
发文量
81
审稿时长
4-8 weeks
期刊介绍: Current Drug Metabolism aims to cover all the latest and outstanding developments in drug metabolism, pharmacokinetics, and drug disposition. The journal serves as an international forum for the publication of full-length/mini review, research articles and guest edited issues in drug metabolism. Current Drug Metabolism is an essential journal for academic, clinical, government and pharmaceutical scientists who wish to be kept informed and up-to-date with the most important developments. The journal covers the following general topic areas: pharmaceutics, pharmacokinetics, toxicology, and most importantly drug metabolism. More specifically, in vitro and in vivo drug metabolism of phase I and phase II enzymes or metabolic pathways; drug-drug interactions and enzyme kinetics; pharmacokinetics, pharmacokinetic-pharmacodynamic modeling, and toxicokinetics; interspecies differences in metabolism or pharmacokinetics, species scaling and extrapolations; drug transporters; target organ toxicity and interindividual variability in drug exposure-response; extrahepatic metabolism; bioactivation, reactive metabolites, and developments for the identification of drug metabolites. Preclinical and clinical reviews describing the drug metabolism and pharmacokinetics of marketed drugs or drug classes.
期刊最新文献
Application of UPLC-MS/MS to Study Cellular Pharmacokinetics of Seven Active Components of Cnidii Fructus Extracts. Drug Metabolizing Enzymes: An Exclusive Guide into Latest Research in Pharmaco-genetic Dynamics in Arab Countries. Unveiling the Interplay: Antioxidant Enzyme Polymorphisms and Oxidative Stress in Preterm Neonatal Renal and Hepatic Functions. Quality by Design Approach for the Development of Cariprazine Hydrochloride Loaded Lipid-Based Formulation for Brain Delivery via Intranasal Route. Ceftobiprole and Cefiderocol for Patients on Extracorporeal Membrane Oxygenation: The Role of Therapeutic Drug Monitoring.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1