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人工智能在药物制剂与研发中的应用及未来展望

Artificial Intelligence in Drug Formulation and Development: Applications and Future Prospects.

作者信息

Srivastava Varsha, Parveen Bushra, Parveen Rabea

机构信息

Department of Pharmaceutics, School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India.

Department of Pharmacognosy and Phytochemistry, Centre of Excellence in Unani Medicine (Pharmacognosy and Pharmacology), School of Pharmaceutical Education and Research, Jamia Hamdard, New Delhi, 110062, India.

出版信息

Curr Drug Metab. 2023;24(9):622-634. doi: 10.2174/0113892002265786230921062205.

Abstract

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.

摘要

人工智能(AI)已成为各个领域的强大工具,药物制剂与研发领域也不例外。这篇综述文章旨在概述人工智能在药物制剂与研发中的应用,并探讨其未来前景。文章开篇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研发过程中的应用,包括药物发现、制造、质量控制、临床试验管理和药物递送等各个领域。人工智能在各种传统剂型以及改良剂型中的应用情况已被汇总。文章还强调了在该领域实施人工智能所面临的挑战和局限性,包括数据可用性、模型可解释性和监管考量。最后,文章介绍了人工智能在药物制剂与研发中的未来前景,强调了个性化医疗、精准药物靶向和快速制剂优化的潜力。文章还讨论了在此背景下人工智能的伦理影响,包括隐私、偏见和问责等问题。

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