Role of Artificial Intelligence in Pharmaceutical Drug Development

Aditya Narayan, Arsh Chanana, Oma Shanker, Yukta R. Kulkarni, Pooja Gupta, Akhilesh Patel, Ujwal Havelikar, Ravindra Pal Singh, H. Chawra
{"title":"Role of Artificial Intelligence in Pharmaceutical Drug Development","authors":"Aditya Narayan, Arsh Chanana, Oma Shanker, Yukta R. Kulkarni, Pooja Gupta, Akhilesh Patel, Ujwal Havelikar, Ravindra Pal Singh, H. Chawra","doi":"10.2174/012210299x313252240521111358","DOIUrl":null,"url":null,"abstract":"\n\nOne of the most popular sectors in the tech and healthcare industries right now is artificial intelligence. In the search and development of new\ndrugs, artificial intelligence is essential. Drug design using computer-assisted design (CADD) has supplanted the traditional approach. Artificial\nintelligence is assisting businesses in the development of new drugs in a faster, more affordable, and more efficient manner, saving money and\nmanpower in the process of creating new drug molecules to treat any disease. Quantitative structure-activity relationship (QSAR) analysis, activity\nscoring, in silico testing, biomarker development, and mode of action identification are all aided by artificial intelligence. It is revolutionizing these\nsectors by swiftly identifying potential drug candidates, efficiently conducting clinical trials, and customizing patient care. AI optimizes drug\nmanufacturing processes, augments safety monitoring, and streamlines market analysis. In clinical trials, AI streamlines patient recruitment and\nensures more precise trial designs, leading to faster and more efficient research. AI empowers personalized medicine by tailoring treatment plans\nand drug dosages to individual patient characteristics. AI also optimizes pharmaceutical manufacturing processes, amplifies safety monitoring by\nanalyzing real-time data for adverse events, and supports market analysis and sales strategies. AI in the pharmaceutical industry is a multifaceted\ntool. Artificial Intelligence (AI) has the potential to streamline complex pharmaceutical regulatory matters. Regulatory processes like audits and\ndossier completion can be automated with AI tools.\n","PeriodicalId":505533,"journal":{"name":"Current Indian Science","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Current Indian Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2174/012210299x313252240521111358","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

One of the most popular sectors in the tech and healthcare industries right now is artificial intelligence. In the search and development of new drugs, artificial intelligence is essential. Drug design using computer-assisted design (CADD) has supplanted the traditional approach. Artificial intelligence is assisting businesses in the development of new drugs in a faster, more affordable, and more efficient manner, saving money and manpower in the process of creating new drug molecules to treat any disease. Quantitative structure-activity relationship (QSAR) analysis, activity scoring, in silico testing, biomarker development, and mode of action identification are all aided by artificial intelligence. It is revolutionizing these sectors by swiftly identifying potential drug candidates, efficiently conducting clinical trials, and customizing patient care. AI optimizes drug manufacturing processes, augments safety monitoring, and streamlines market analysis. In clinical trials, AI streamlines patient recruitment and ensures more precise trial designs, leading to faster and more efficient research. AI empowers personalized medicine by tailoring treatment plans and drug dosages to individual patient characteristics. AI also optimizes pharmaceutical manufacturing processes, amplifies safety monitoring by analyzing real-time data for adverse events, and supports market analysis and sales strategies. AI in the pharmaceutical industry is a multifaceted tool. Artificial Intelligence (AI) has the potential to streamline complex pharmaceutical regulatory matters. Regulatory processes like audits and dossier completion can be automated with AI tools.
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
人工智能在药物开发中的作用
人工智能是当前科技和医疗保健行业最热门的领域之一。在寻找和开发新药的过程中,人工智能是必不可少的。使用计算机辅助设计(CADD)进行药物设计已经取代了传统方法。人工智能正在协助企业以更快、更实惠、更高效的方式开发新药,在创造治疗任何疾病的新药物分子的过程中节省资金和人力。定量结构-活性关系(QSAR)分析、活性评分、硅学测试、生物标记开发和作用模式识别都离不开人工智能的帮助。人工智能通过快速识别潜在候选药物、高效开展临床试验和定制病人护理,正在彻底改变这些领域。人工智能优化了药品生产流程,增强了安全性监测,并简化了市场分析。在临床试验中,人工智能可简化患者招募,确保更精确的试验设计,从而实现更快、更高效的研究。人工智能可根据患者的个体特征量身定制治疗方案和药物剂量,从而实现个性化医疗。人工智能还能优化制药流程,通过分析不良事件的实时数据加强安全监控,并支持市场分析和销售策略。制药行业的人工智能是一个多面手。人工智能有可能简化复杂的制药监管事务。利用人工智能工具,审计和档案填写等监管流程可以实现自动化。
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
An Overview of Optical Sensing of Copper and its Removal by Different Techniques Role of Artificial Intelligence in Pharmaceutical Drug Development Recent Developments in the Use of Spinel Ferrite Nanoparticles as Catalysts in Organic Reactions Evaluation of 3, 3’-Disubstituted Oxindoles Derivatives as a Potential Anti- Cancer Tyrosine Kinase Inhibitors-Molecular Docking and ADME Studies Computational Molecular Docking and In-Silico, ADMET Prediction Studies of Quinoline Derivatives as EPHB4 Inhibitor
×
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