Artificial Intelligence in Pharmaceutical Drug Development: Transforming Formulation and Innovation.

Dushyant, Smita Narwal, Vishakha Saini, Ashwani K Dhingra, Jagdeep Singh, Jasmeen, Rupa Devi, Shabnam
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Abstract

Artificial intelligence (AI) has developed into a powerful tool that employs human knowledge to swiftly resolve complex issues. Significant advancements in computer learning and artificial intelligence present a revolutionary opportunity for pharmaceutical formulation, drug dis-covery, and dosage form testing. AI algorithms would analyze a tremendous amount of biological reference data, such as proteomics and genomes, to assist researchers in identifying target diseases and predicting their possible interactions with proposed approaches to treatment. Just such focused and efficient drug development significantly augments the likelihood of acquiring drug approvals. AI may add, at the same time, develop costs and streamline research and development processes. Clas-sical machine learning techniques help not only in designing the experiment but also significantly in predicting the pharmacokinetics and toxicity of new drugs. This ability reduces the need for expen-sive, time-consuming animal testing by selection with optimization of lead compounds. Personalized medical strategies based on actual patient data assessments by algorithms such as those of AI may benefit patients through increased treatment adherence and outcomes. The review covers many ap-plications of AI for process optimization, testing, drug delivery dosage form design, and drug discov-ery. This study underlines the benefits brought by numerous types of techniques based on AI in phar-maceutical technology. However, there are exciting prospects to enhance patient care and medication development processes because of the pharmaceutical industry's continuous investment in and re-search into AI.

药物开发中的人工智能:改变配方和创新。
人工智能(AI)已经发展成为利用人类知识快速解决复杂问题的强大工具。计算机学习和人工智能的重大进步为药物配方、药物发现和剂型测试提供了革命性的机会。人工智能算法将分析大量的生物参考数据,如蛋白质组学和基因组,以帮助研究人员识别目标疾病,并预测它们与拟议治疗方法的可能相互作用。正是这种专注和高效的药物开发大大增加了获得药物批准的可能性。与此同时,人工智能可能会增加成本,并简化研发过程。经典的机器学习技术不仅有助于设计实验,而且在预测新药的药代动力学和毒性方面也有重要意义。这种能力减少了对昂贵、耗时的动物试验的需要,通过选择和优化先导化合物。基于人工智能等算法的实际患者数据评估的个性化医疗策略可能会通过提高治疗依从性和结果使患者受益。该综述涵盖了人工智能在工艺优化、检测、给药剂型设计和药物发现方面的许多应用。这项研究强调了基于人工智能的多种技术在制药技术中带来的好处。然而,由于制药行业对人工智能的持续投资和研究,在加强患者护理和药物开发过程方面有令人兴奋的前景。
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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