The Role of Deep Learning in Pharma: Revolutionizing Drug Discovery and Development.

Q3 Engineering
Joti Devi Et al.
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 Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.","PeriodicalId":39883,"journal":{"name":"推进技术","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"推进技术","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.52783/tjjpt.v44.i3.257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

Abstract

- In recent years, the pharmaceutical industry has witnessed a transformative shift in the way drugs are discovered and developed, thanks to the advent of deep learning. This paper explores the profound impact of deep learning techniques on various stages of drug discovery and development, from target identification and lead optimization to clinical trials and personalized medicine. Deep learning, a subset of artificial intelligence, has demonstrated exceptional capabilities in handling complex biological data, including genomics, proteomics, and chemical informatics. It enables the integration of vast and diverse datasets, facilitating the identification of potential drug targets with unprecedented accuracy. Moreover, deep learning models can predict the binding affinity of drug candidates to specific target proteins, expediting the lead optimization process and reducing the need for costly experimental iterations. Deep learning algorithms enhance patient stratification and biomarker discovery, ultimately leading to more successful trials with higher patient response rates. Additionally, the ability to analyze real-world patient data aids in the identification of adverse events and the development of safer drugs. Perrsonalized medicine is another area greatly influenced by deep learning, as it allows for tailoring treatments to individual patients based on their unique genetic and clinical profiles. This promises to revolutionize patient care, optimizing therapeutic outcomes while minimizing adverse effects. Despite the remarkable advancements facilitated by deep learning, there are challenges to address, such as data privacy, interpretability of models, and regulatory considerations. This paper discusses these challenges and potential solutions. Deep learning has emerged as a powerful tool in the pharmaceutical industry, driving innovation, efficiency, and precision in drug discovery and development. Its integration into the drug development pipeline holds the promise of accelerating the delivery of safer and more effective therapies to patients worldwide, marking a significant milestone in the evolution of pharmaceutical science.
深度学习在制药中的作用:革命性的药物发现和开发。
-近年来,由于深度学习的出现,制药行业见证了药物发现和开发方式的革命性转变。本文探讨了深度学习技术在药物发现和开发的各个阶段的深远影响,从靶点识别和先导物优化到临床试验和个性化医疗。深度学习是人工智能的一个子集,在处理复杂的生物数据方面表现出了非凡的能力,包括基因组学、蛋白质组学和化学信息学。它能够整合大量不同的数据集,以前所未有的准确性促进潜在药物靶点的识别。此外,深度学习模型可以预测候选药物与特定靶蛋白的结合亲和力,加快先导物优化过程,减少昂贵的实验迭代需求。深度学习算法增强了患者分层和生物标志物的发现,最终导致更多成功的试验和更高的患者反应率。此外,分析真实世界患者数据的能力有助于识别不良事件和开发更安全的药物。 个性化医疗是另一个深受深度学习影响的领域,因为它允许根据患者独特的基因和临床概况为个体患者量身定制治疗。这有望彻底改变患者护理,优化治疗结果,同时最大限度地减少不良反应。尽管深度学习带来了显著的进步,但仍有一些挑战需要解决,比如数据隐私、模型的可解释性和监管方面的考虑。本文讨论了这些挑战和潜在的解决方案。深度学习已经成为制药行业的一个强大工具,推动了药物发现和开发的创新、效率和准确性。将其整合到药物开发管道中,有望加速向全球患者提供更安全、更有效的治疗方法,这是制药科学发展的一个重要里程碑。
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
推进技术
推进技术 Engineering-Aerospace Engineering
CiteScore
1.40
自引率
0.00%
发文量
6610
期刊介绍: "Propulsion Technology" is supervised by China Aerospace Science and Industry Corporation and sponsored by the 31st Institute of China Aerospace Science and Industry Corporation. It is an important journal of Chinese degree and graduate education determined by the Academic Degree Committee of the State Council and the State Education Commission. It was founded in 1980 and is a monthly publication, which is publicly distributed at home and abroad. Purpose of the publication: Adhere to the principles of quality, specialization, standardized editing, and scientific management, publish academic papers on theoretical research, design, and testing of various aircraft, UAVs, missiles, launch vehicles, spacecraft, and ship propulsion systems, and promote the development and progress of turbines, ramjets, rockets, detonation, lasers, nuclear energy, electric propulsion, joint propulsion, new concepts, and new propulsion technologies.
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