A review on artificial intelligence and machine learning used in pharmaceutical research

Utkarsha A. Wadighare, Swati P. Deshmukh
{"title":"A review on artificial intelligence and machine learning used in pharmaceutical research","authors":"Utkarsha A. Wadighare, Swati P. Deshmukh","doi":"10.30574/gscbps.2024.26.1.0446","DOIUrl":null,"url":null,"abstract":"The cutting-edge upward push of artificial intelligence and system mastering has been of considerable size. It has reduced the human workload move forward exceptional of life exceptionally. This article describes using artificial intelligence and system learning to augment drug discovery and upgrade to lead them to more well organised and correct. In medication, specialties in which images are vitally important, like radiology, pathology or oncology, have seized the able to be done and full-size efforts in studies and development were deployed to switch the adaptness of AI to scientific packages. With AI becoming a extra widespread device for usual scientific imaging evaluation duties, together with prognosis, segmentation, or classification, the important thing for a secure and efficient use of medical AI packages. This body of work supported the jobs of system gaining knowledge of and synthetic intelligence in facilitating drug expansion and finding out methods, making them greater cost-powerful or altogether casting off the want for clinical trials, as a result of the potential to conduct simulations the usage of those technologies. Doing so will assist in separating wish from hype and lead to knowledgeable choice making at the top-quality use of AI/ML in drug development. Machine studying strategies can subterfuge complicated analyzes with huge, heterogeneous, and excessive dimensional information collections without a guide enter, which has proved helpful inside the writing commercial enterprise applications. Combining system mastering, particularly deep getting to know, with human skill and revel in is probably the great manner to coordinate numerous significant facts stores. The magnificent facts-mining capacity of AI innovation has given new essentiality to computer supported medication plans that incorporate more than one clinical concerns are higher than piecemeal data.","PeriodicalId":12808,"journal":{"name":"GSC Biological and Pharmaceutical Sciences","volume":"2 3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"GSC Biological and Pharmaceutical Sciences","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.30574/gscbps.2024.26.1.0446","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The cutting-edge upward push of artificial intelligence and system mastering has been of considerable size. It has reduced the human workload move forward exceptional of life exceptionally. This article describes using artificial intelligence and system learning to augment drug discovery and upgrade to lead them to more well organised and correct. In medication, specialties in which images are vitally important, like radiology, pathology or oncology, have seized the able to be done and full-size efforts in studies and development were deployed to switch the adaptness of AI to scientific packages. With AI becoming a extra widespread device for usual scientific imaging evaluation duties, together with prognosis, segmentation, or classification, the important thing for a secure and efficient use of medical AI packages. This body of work supported the jobs of system gaining knowledge of and synthetic intelligence in facilitating drug expansion and finding out methods, making them greater cost-powerful or altogether casting off the want for clinical trials, as a result of the potential to conduct simulations the usage of those technologies. Doing so will assist in separating wish from hype and lead to knowledgeable choice making at the top-quality use of AI/ML in drug development. Machine studying strategies can subterfuge complicated analyzes with huge, heterogeneous, and excessive dimensional information collections without a guide enter, which has proved helpful inside the writing commercial enterprise applications. Combining system mastering, particularly deep getting to know, with human skill and revel in is probably the great manner to coordinate numerous significant facts stores. The magnificent facts-mining capacity of AI innovation has given new essentiality to computer supported medication plans that incorporate more than one clinical concerns are higher than piecemeal data.
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
制药研究中使用的人工智能和机器学习综述
人工智能和系统掌握的前沿技术已经有了相当大的发展。它减少了人类的工作量,异常地推进了人类的生活。本文介绍了利用人工智能和系统学习来增强药物发现和升级,从而使其更有组织、更正确。在医学领域,图像至关重要的专业,如放射学、病理学或肿瘤学,已经抓住了这一能够完成的任务,并在研究和开发方面部署了全面的努力,以将人工智能的适应性转换到科学包中。随着人工智能越来越广泛地应用于通常的科学成像评估工作,包括预后、分割或分类,安全高效地使用医疗人工智能软件包就显得尤为重要。这些工作支持系统获取知识和合成智能在促进药物开发和研究方法方面的工作,使其更具成本效益,或完全摆脱对临床试验的需求,因为利用这些技术可以进行模拟。这样做将有助于把愿望与炒作区分开来,并在药物开发中高质量地使用人工智能/ML 方面做出明智的选择。机器学习策略可以在没有指导输入的情况下,对庞大、异构和高维的信息集合进行复杂的潜行分析,这已被证明有助于编写商业企业应用程序。将系统掌握(尤其是深度掌握)与人类技能和经验相结合,可能是协调众多重要信息库的最佳方式。人工智能创新的强大事实挖掘能力为计算机支持的医疗计划赋予了新的本质,这些计划包含多个临床问题,高于零散的数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
FTIR characterization of Siddha medicine Komoothira silasathu parpam Antimicrobial effect of aqueous extracts of Garcinia kola, Cymbopogan citratus and Bryophyllium pinnatum against sputum bacterial isolates from human subjects Toxicological and analgesic evaluation of Solanecio biafrae ethanol leaf extract Methicillin-Sensible Staphylococcus aureus (MSSA) Pericardial Effusion Causing Cardiac Tamponade: A case report Verification of the insulin dosage method using Abbott Alinity ci®: experience of the biochemistry laboratory, CHU Mohammed IV Oujda, Morocco
×
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