Reference [1]  参考资料 [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect
报告题目：人工智能在医药监管事务中的应用 - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167
网址：https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.
亮点：人工智能 （AI） 正在迅速改变制药行业，监管事务也不例外。人工智能在药品监管事务中的使用仍处于早期阶段，但它有可能彻底改变该行业。将 AI 整合到监管事务将有助于及时了解最新的行业趋势，并了解 AI 在药物发现及其监管批准方面的潜在好处。反过来，监管机构根据从药物发现到产品开发的所有阶段提交的数据和文件来批准产品。自动化可以减少从记录中收集、分离和标准化数据所需的时间，以及减少文档过程中对人工参与的要求，从而帮助加快这一过程。AI 是为执行需要人类智能的任务而开发的软件或计算机程序，例如视觉感知、语音识别、决策和语言翻译。它是一个多学科的 fi 传统的监管流程很慢，AI 已经应用于制药行业监管事务以外的领域。将 AI 与人类智能相结合具有巨大的潜力，可以最大限度地利用可用于监管审批战略规划的时间。将 AI 整合到监管事务将有助于及时了解最新的行业趋势，并了解 AI 在药物发现及其监管批准方面的潜在好处。AI 可以通过自动执行任务、改进决策和发现新机会，帮助药物发现专业人员更快、更高效地将新药推向市场。 监管事务是指制药公司内部的团队或职能部门，他们与东道国或其他地方的监管机构建立联系，他们努力根据现行法规获得产品批准。

Reference [2]  参考资料 [2]

Title: Artificial Intelligence (AI) in Drugs and Pharmaceuticals - PubMed
报告题目：人工智能 （AI） 在药物和制药中的应用 - PubMed

Url: https://pubmed.ncbi.nlm.nih.gov/34875986/
网址：https://pubmed.ncbi.nlm.nih.gov/34875986/

Highlights: Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system.
亮点： 因此，AI 已被用于药物分子特性（吸收、分布、代谢、排泄和毒性）的从头药物设计、活性评分、虚拟筛选和计算机评估。各种制药公司与 AI 公司合作，以加快药物开发领域以及医疗保健系统的进展。

Reference [3]  参考资料 [3]

Title: Machine learning applications in drug development - ScienceDirect
报告题目：机器学习在药物开发中的应用 - ScienceDirect

Url: https://www.sciencedirect.com/science/article/pii/S2001037019303988
网址：https://www.sciencedirect.com/science/article/pii/S2001037019303988

Highlights: These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face.
亮点：这些管道可以指导或加速药物发现;更好地了解疾病和相关生物现象;帮助规划临床前 WET-LAB 实验，甚至未来的临床试验。这种药物开发过程的自动化可能是制药公司目前面临的低生产率问题的关键。在这项调查中，我们将特别关注两类方法：顺序学习和推荐系统，它们是活跃的生物医学研究领域。由于当今可用的大量生物和医学数据，以及成熟的机器学习算法，现在可以设想在很大程度上自动化的药物开发管道的设计。这些管道可以指导或加速药物发现;更好地了解疾病和相关生物现象;帮助规划临床前 WET-LAB 实验，甚至未来的临床试验。这种药物开发过程的自动化可能是制药公司目前面临的低生产率问题的关键。

Reference [2]  参考资料 [2]

Title: Artificial Intelligence (AI) in Drugs and Pharmaceuticals - PubMed
报告题目：人工智能 （AI） 在药物和制药中的应用 - PubMed

Url: https://pubmed.ncbi.nlm.nih.gov/34875986/
网址：https://pubmed.ncbi.nlm.nih.gov/34875986/

Highlights: Hence, AI has been used in de novo drug design, activity scoring, virtual screening and in silico evaluation in the properties (absorption, distribution, metabolism, excretion and toxicity) of a drug molecule. Various pharmaceutical companies have teamed up with AI companies for faster progress in the field of drug development, along with the healthcare system.
亮点： 因此，AI 已被用于药物分子特性（吸收、分布、代谢、排泄和毒性）的从头药物设计、活性评分、虚拟筛选和计算机评估。各种制药公司与 AI 公司合作，以加快药物开发领域以及医疗保健系统的进展。

Reference [1]  参考资料 [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect
报告题目：人工智能在医药监管事务中的应用 - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167
网址：https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.
亮点：人工智能 （AI） 正在迅速改变制药行业，监管事务也不例外。人工智能在药品监管事务中的使用仍处于早期阶段，但它有可能彻底改变该行业。将 AI 整合到监管事务将有助于及时了解最新的行业趋势，并了解 AI 在药物发现及其监管批准方面的潜在好处。反过来，监管机构根据从药物发现到产品开发的所有阶段提交的数据和文件来批准产品。自动化可以减少从记录中收集、分离和标准化数据所需的时间，以及减少文档过程中对人工参与的要求，从而帮助加快这一过程。AI 是为执行需要人类智能的任务而开发的软件或计算机程序，例如视觉感知、语音识别、决策和语言翻译。它是一个多学科的 fi 传统的监管流程很慢，AI 已经应用于制药行业监管事务以外的领域。将 AI 与人类智能相结合具有巨大的潜力，可以最大限度地利用可用于监管审批战略规划的时间。将 AI 整合到监管事务将有助于及时了解最新的行业趋势，并了解 AI 在药物发现及其监管批准方面的潜在好处。AI 可以通过自动执行任务、改进决策和发现新机会，帮助药物发现专业人员更快、更高效地将新药推向市场。 监管事务是指制药公司内部的团队或职能部门，他们与东道国或其他地方的监管机构建立联系，他们努力根据现行法规获得产品批准。

Reference [4]  参考资料 [4]

Title: Artificial Intelligence (AI) in Pharma: How to Use It in 2024
报告题目：制药领域的人工智能 （AI）：2024 年如何使用

Url: https://viseven.com/artificial-intelligence-in-pharma-industry/
网址：https://viseven.com/artificial-intelligence-in-pharma-industry/

Highlights: AI algorithms and machine learning models have a significant impact on the biotech industry. From life-saving drugs discovery, development, and production to clinical trials, communication, and drug target identification — AI pharmaceutical is a definite game-changer. According to The McKinsey Global Institute’s research, the influence of AI and machine learning on the pharma market generated around $100B across the US healthcare system in 2021.Also, artificial intelligence pharmaceutical automation allows your company to perform predictive maintenance and quality control of drug combinations. There’s no sign of this trend slowing down — on the contrary, about 50 percent of global healthcare companies plan to implement AI strategies and broadly adopt the technology by 2025.
亮点： AI 算法和机器学习模型对生物技术行业有重大影响。从拯救生命的药物发现、开发和生产到临床试验、沟通和药物靶点识别，AI 制药无疑会改变游戏规则。根据麦肯锡全球研究所的研究，人工智能和机器学习对制药市场的影响在 2021 年在整个美国医疗保健系统中产生了约 100B 美元。此外，人工智能制药自动化允许您的公司对药物组合进行预测性维护和质量控制。这一趋势没有放缓的迹象——相反，大约 50% 的全球医疗保健公司计划到 2025 年实施 AI 战略并广泛采用该技术。

Reference [3]  参考资料 [3]

Title: Machine learning applications in drug development - ScienceDirect
报告题目：机器学习在药物开发中的应用 - ScienceDirect

Url: https://www.sciencedirect.com/science/article/pii/S2001037019303988
网址：https://www.sciencedirect.com/science/article/pii/S2001037019303988

Highlights: These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face. In this survey, we will particularly focus on two classes of methods: sequential learning and recommender systems, which are active biomedical fields of research.Due to the huge amount of biological and medical data available today, along with well-established machine learning algorithms, the design of largely automated drug development pipelines can now be envisioned. These pipelines may guide, or speed up, drug discovery; provide a better understanding of diseases and associated biological phenomena; help planning preclinical wet-lab experiments, and even future clinical trials. This automation of the drug development process might be key to the current issue of low productivity rate that pharmaceutical companies currently face.
亮点：这些管道可以指导或加速药物发现;更好地了解疾病和相关生物现象;帮助规划临床前 WET-LAB 实验，甚至未来的临床试验。这种药物开发过程的自动化可能是制药公司目前面临的低生产率问题的关键。在这项调查中，我们将特别关注两类方法：顺序学习和推荐系统，它们是活跃的生物医学研究领域。由于当今可用的大量生物和医学数据，以及成熟的机器学习算法，现在可以设想在很大程度上自动化的药物开发管道的设计。这些管道可以指导或加速药物发现;更好地了解疾病和相关生物现象;帮助规划临床前 WET-LAB 实验，甚至未来的临床试验。这种药物开发过程的自动化可能是制药公司目前面临的低生产率问题的关键。

Reference [5]  参考资料 [5]

Title: Leveraging AI for drug development through regulatory intelligence | ZS
报告题目：通过监管智能利用 AI 进行药物开发 |ZS

Url: https://www.zs.com/insights/technology-regulatory-intelligence-accelerate-drug-development
网址：https://www.zs.com/insights/technology-regulatory-intelligence-accelerate-drug-development

Highlights: Currently, the regulatory intelligence process is almost entirely manual, but we believe that technology-aided regulatory intelligence tools could greatly improve the drug development process. But to make progress here, pharma companies and regulatory affairs subject matter experts (SMEs) need to be aware of how existing technologies could help them and remain open to change. The journey from study completion to regulatory approval and market authorization in all the countries a company targets for launch is a long one. But there are numerous ways to speed this up—from selecting the appropriate drug approval pathway to utilizing different accelerated trial design options.If pharma companies are willing to put thought and effort into seeking out technology solutions for these processes, we believe that digitizing these steps could accelerate drug development timelines by 10 months. They can also ensure that companies make the most optimal regulatory strategy decisions and use the most appropriate regulatory pathway. But technology by itself can’t succeed without the processes and people trained to work alongside it.
亮点：目前，监管情报流程几乎完全是手动的，但我们相信技术辅助的监管情报工具可以大大改善药物开发流程。但是，要在这方面取得进展，制药公司和监管事务主题专家 （SME） 需要了解现有技术如何帮助他们，并对变化保持开放态度。从研究完成到监管批准和在公司目标所有国家/地区获得市场授权，这是一个漫长的过程。但是，有许多方法可以加快这一过程——从选择合适的药物批准途径到利用不同的加速试验设计选项。如果制药公司愿意投入思考和努力为这些流程寻找技术解决方案，我们相信将这些步骤数字化可以将药物开发时间缩短 10 个月。他们还可以确保公司做出最佳的监管战略决策，并使用最合适的监管途径。但是，如果没有经过培训的流程和人员，技术本身就无法成功。

Reference [6]  参考资料 [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC
报告题目：人工智能在制药技术和药物递送设计中的应用 - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/
网址：https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR
亮点： AI 涉及使用高级工具和软件来实现类似人类的能力。这种创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。它还提供了一个更好的平台来了解工艺参数对产品配方和制造的影响。这种创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。供应链优化： 人工智能用于优化药品供应链，确保高效的制造、库存管理和分销。AI 算法可以预测需求、优化生产计划并增强质量控制流程，从而有助于实现更简化和更具成本效益的运营。制药行业是一个关键领域，在挽救生命方面发挥着至关重要的作用。它的运作基于持续创新和采用新技术来应对全球医疗保健挑战并应对医疗紧急情况，例如最近的大流行 [1]。在制药行业，创新通常基于各个领域的广泛研究和开发，包括但不限于制造技术、包装考虑因素和以客户为导向的营销策略 [2]。 新型药物创新范围从小药分子到生物制剂，偏爱更好的稳定性和高效性，以满足治疗疾病的未满足需求。描述了应对制药行业挑战的可能人工智能 （AI） 解决方案：获得熟练的劳动力是所有部门利用他们在产品创新方面的专业知识、熟练程度和才能的先决条件。第二个与供应链中断和临床试验实验挑战有关。网络攻击的发生率呈上升趋势，数据泄露和安全成为该行业的重大关注点。大流行的主要影响正在消退，但它仍然对临床试验产生一些影响。尽管如此，制药行业对 AI 的持续投资和探索为改进药物开发流程和患者护理提供了令人兴奋的前景。关键词：人工智能 （AI）、机器学习、药物发现、配方、剂型测试、药代动力学、药效学、PBPK、QSAR 创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。它还提供了一个更好的平台来了解工艺参数对产品配方和制造的影响。AI 技术和机器学习的显著进步为药物发现、配方和药物剂型测试带来了变革性的机会。 通过利用分析大量生物数据（包括基因组学和蛋白质组学）的 AI 算法，研究人员可以识别与疾病相关的靶点并预测它们与潜在候选药物的相互作用。这为药物发现提供了一种更有效、更有针对性的方法，从而提高了药物成功批准的可能性。此外，AI 可以通过优化研发流程来降低开发成本。这篇综述概述了制药技术中使用的各种基于 AI 的方法，强调了它们的优点和缺点。尽管如此，制药行业对 AI 的持续投资和探索为改进药物开发流程和患者护理提供了令人兴奋的前景。关键词：人工智能 （AI）， 机器学习， 药物发现， 配方， 剂型测试， 药代动力学， 药效学， PBPK， QSAR

Reference [6]  参考资料 [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC
报告题目：人工智能在制药技术和药物递送设计中的应用 - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/
网址：https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR
亮点： AI 涉及使用高级工具和软件来实现类似人类的能力。这种创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。它还提供了一个更好的平台来了解工艺参数对产品配方和制造的影响。这种创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。供应链优化： 人工智能用于优化药品供应链，确保高效的制造、库存管理和分销。AI 算法可以预测需求、优化生产计划并增强质量控制流程，从而有助于实现更简化和更具成本效益的运营。制药行业是一个关键领域，在挽救生命方面发挥着至关重要的作用。它的运作基于持续创新和采用新技术来应对全球医疗保健挑战并应对医疗紧急情况，例如最近的大流行 [1]。在制药行业，创新通常基于各个领域的广泛研究和开发，包括但不限于制造技术、包装考虑因素和以客户为导向的营销策略 [2]。 新型药物创新范围从小药分子到生物制剂，偏爱更好的稳定性和高效性，以满足治疗疾病的未满足需求。描述了应对制药行业挑战的可能人工智能 （AI） 解决方案：获得熟练的劳动力是所有部门利用他们在产品创新方面的专业知识、熟练程度和才能的先决条件。第二个与供应链中断和临床试验实验挑战有关。网络攻击的发生率呈上升趋势，数据泄露和安全成为该行业的重大关注点。大流行的主要影响正在消退，但它仍然对临床试验产生一些影响。尽管如此，制药行业对 AI 的持续投资和探索为改进药物开发流程和患者护理提供了令人兴奋的前景。关键词：人工智能 （AI）、机器学习、药物发现、配方、剂型测试、药代动力学、药效学、PBPK、QSAR 创新在许多领域都有帮助，例如制药行业，尤其是在过去几年的产品开发阶段。实施这些技术创新可以节省制造和通过供应链正确分销给最终客户所需的时间、金钱和资源。它还提供了一个更好的平台来了解工艺参数对产品配方和制造的影响。AI 技术和机器学习的显著进步为药物发现、配方和药物剂型测试带来了变革性的机会。 通过利用分析大量生物数据（包括基因组学和蛋白质组学）的 AI 算法，研究人员可以识别与疾病相关的靶点并预测它们与潜在候选药物的相互作用。这为药物发现提供了一种更有效、更有针对性的方法，从而提高了药物成功批准的可能性。此外，AI 可以通过优化研发流程来降低开发成本。这篇综述概述了制药技术中使用的各种基于 AI 的方法，强调了它们的优点和缺点。尽管如此，制药行业对 AI 的持续投资和探索为改进药物开发流程和患者护理提供了令人兴奋的前景。关键词：人工智能 （AI）， 机器学习， 药物发现， 配方， 剂型测试， 药代动力学， 药效学， PBPK， QSAR

Reference [7]  参考资料 [7]

Title: Artificial intelligence in drug discovery and development - PMC
报告题目：人工智能在药物发现和开发中的应用 - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/
网址：https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/

Highlights: Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design [16]; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development [17]. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predThis review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period.Applications of artificial intelligence (AI) in different subfields of the pharmaceutical industry, from drug discovery to pharmaceutical product management. The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules [19]. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI [15]. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design 19, 20.This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the tools and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them, along with the future of AI in the pharmaceutical industry, is also discussed.
亮点：人工智能参与从实验室到床边的药品开发是可以想象的，因为它可以帮助合理的药物设计 [16];协助决策;为患者确定正确的治疗方法，包括个性化药物;并管理生成的临床数据并将其用于未来的药物开发 [17]。E-VAI 是由 Eularis 开发的分析和决策 AI 平台，它使用 ML 算法以及易于使用的用户界面，根据竞争对手、主要利益相关者和目前对 pred 的市场份额创建分析路线图这篇评论强调了 AI 在制药行业不同领域（即药物发现和开发）中的影响使用， 药物再利用、提高制药生产力、临床试验等，从而减少人力负担并在短时间内实现目标。人工智能 （AI） 在制药行业不同子领域的应用，从药物发现到药品管理。由 >1060 分子组成的巨大化学空间促进了大量药物分子的开发 [19]。然而，缺乏先进技术限制了药物开发过程，使其成为一项耗时且昂贵的任务，这可以通过使用 AI 来解决 [15]。人工智能可以识别苗头化合物和先导化合物，并更快地验证药物靶点和优化药物结构设计 19， 20.本综述重点介绍了人工智能在制药行业不同领域的影响应用，即药物发现和开发、药物再利用、提高药物生产率、临床试验等。 仅举几例，从而减少人力负担并在短时间内实现目标。还讨论了用于实施 AI 的工具和技术、持续的挑战和克服这些挑战的方法，以及 AI 在制药行业的未来。

Reference [8]  参考资料 [8]

Title: How Artificial Intelligence is Revolutionizing Drug Discovery - Bill of Health
报告题目：人工智能如何彻底改变药物研发 - Bill of Health

Url: https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/
网址：https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/

Highlights: Traditional drug discovery is a notoriously time consuming and expensive process, with pre-clinical stages typically taking three to six years and costing hundreds of millions to billions of dollars. However, a host of AI tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.Far from being a distant sci-fi future, AI-enabled drug discovery is already here. A non-exhaustive list of historic milestones in the field includes the following achievements: In early 2020, Exscientia announced the first-ever AI-designed drug molecule to enter human clinical trials.Prediction of drug properties: Some AI systems are being used to bypass simulated testing of drug candidates by predicting key properties such as toxicity, bioactivity, and the physicochemical characteristics of molecules. De novo drug design: While traditional drug discovery has historically involved the screening of large libraries of candidate molecules, AI is shifting this paradigm too.
亮点：众所周知，传统药物发现是一个耗时且昂贵的过程，临床前阶段通常需要三到六年，成本高达数亿至数十亿美元。然而，许多 AI 工具正在彻底改变药物发现过程的几乎每个阶段，为重塑行业的速度和经济性提供了巨大的潜力。AI 支持的药物发现远非遥远的科幻未来，它已经到来。该领域历史里程碑的非详尽清单包括以下成就：2020 年初，Exscientia 宣布了有史以来第一个进入人体临床试验的人工智能设计药物分子。药物特性预测：一些 AI 系统被用于通过预测关键特性（如毒性、生物活性和分子的物理化学特性）来绕过候选药物的模拟测试。从头药物设计：虽然传统的药物发现历来涉及筛选大型候选分子库，但 AI 也在改变这种范式。

Reference [8]  参考资料 [8]

Title: How Artificial Intelligence is Revolutionizing Drug Discovery - Bill of Health
报告题目：人工智能如何彻底改变药物研发 - Bill of Health

Url: https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/
网址：https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/

Highlights: Traditional drug discovery is a notoriously time consuming and expensive process, with pre-clinical stages typically taking three to six years and costing hundreds of millions to billions of dollars. However, a host of AI tools are revolutionizing nearly every stage of the drug discovery process, offering substantial potential to reshape the speed and economics of the industry.Far from being a distant sci-fi future, AI-enabled drug discovery is already here. A non-exhaustive list of historic milestones in the field includes the following achievements: In early 2020, Exscientia announced the first-ever AI-designed drug molecule to enter human clinical trials.Prediction of drug properties: Some AI systems are being used to bypass simulated testing of drug candidates by predicting key properties such as toxicity, bioactivity, and the physicochemical characteristics of molecules. De novo drug design: While traditional drug discovery has historically involved the screening of large libraries of candidate molecules, AI is shifting this paradigm too.
亮点：众所周知，传统药物发现是一个耗时且昂贵的过程，临床前阶段通常需要三到六年，成本高达数亿至数十亿美元。然而，许多 AI 工具正在彻底改变药物发现过程的几乎每个阶段，为重塑行业的速度和经济性提供了巨大的潜力。AI 支持的药物发现远非遥远的科幻未来，它已经到来。该领域历史里程碑的非详尽清单包括以下成就：2020 年初，Exscientia 宣布了有史以来第一个进入人体临床试验的人工智能设计药物分子。药物特性预测：一些 AI 系统被用于通过预测关键特性（如毒性、生物活性和分子的物理化学特性）来绕过候选药物的模拟测试。从头药物设计：虽然传统的药物发现历来涉及筛选大型候选分子库，但 AI 也在改变这种范式。

Reference [7]

Title: Artificial intelligence in drug discovery and development - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/

Highlights: Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design [16]; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development [17]. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predThis review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period.Applications of artificial intelligence (AI) in different subfields of the pharmaceutical industry, from drug discovery to pharmaceutical product management. The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules [19]. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI [15]. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design 19, 20.This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the tools and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them, along with the future of AI in the pharmaceutical industry, is also discussed.

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [7]

Title: Artificial intelligence in drug discovery and development - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/

Highlights: Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design [16]; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development [17]. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predThis review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period.Applications of artificial intelligence (AI) in different subfields of the pharmaceutical industry, from drug discovery to pharmaceutical product management. The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules [19]. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI [15]. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design 19, 20.This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the tools and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them, along with the future of AI in the pharmaceutical industry, is also discussed.

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [9]

Title: AI in drug discovery and its clinical relevance - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10302550/

Highlights: Motivation for this survey: Multiple reviews are available on the application of AI in drug discovery. For example, the role of GPU computing and deep learning models for drug discovery is presented in [13], deep learning for precision medicine in [14], generative models for calculating the electronic properties of materials in [15], the advancements due to the completion of human genome project in [8]. The role of machine learning and its implications in drug discovery for understanding biological interactions is presented in [16]. Methods using 3D structure-based drug discovery and dynamicsDrug discovery has been radically transformed over the last decade by such novel analytical methods and computational advances [6] [7] [8] [9] [10]. Due to recent progress, there is a great interest in the application of artificial intelligence (AI) methods to improve various stages of drug discovery pipeline, including de novo molecular design and optimization, structure-based drug design, and pre-clinical and clinical development [11]. Biomedical datasets, such as genomic profiles, imaging data, and chemical and drug databases, can be coupled with analytical methods, especially deep learning models, to coordinate the tools needed to discover useful drugs and their clinical applications [12].Both networks are trained individually but used together to produce innovative targeted chemical libraries, based on deep RL techniques. The completion of the human genome project [14] resulted in the explosion of genomic, proteomic, and structural data. Excellent drug targets are being identified at a faster rate and low cost due to advances in bioinformatics and data analytics methods [123]. Computational structure-based drug design takes advantage of the accumulation of biological data, such as structures of proteins (Protein Data Bank) and drug databanks (DrugBank).Medical data generated from large molecular screening profiles, personal health or pathology records, and public health organizations could benefit from analysis by Artificial Intelligence (AI) approaches to speed up and prevent failures in the drug discovery pipeline. We present applications of AI at various stages of drug discovery pipelines, including the inherently computational approaches of de novo design and prediction of a drug's likely properties.Therefore devising a system, learning the true representation, and labeling the data are major challenges for the success of AI in the drug discovery domain. Many deep learning systems also suffer from repeatability crisis [155] due to stochastic initialization and optimization of parameters, which can be sensitive to the initial settings. The type of learning paradigms and evaluation metrics are also important since biological datasets are imbalanced, complex, partially labeled, and not fully understood. Unsupervised or semi-supervised learning can be used to address these challenges and to generate hypotheses for understanding complex diseases and signaling pathways patterns [156]. We also hypothesize that over-fitted machine learning models may generate a novel data-driven hypothesis, which can be validated with experimental Biologists.

Reference [10]

Title: AI in Drug Discovery: Top Cases Transforming the Industry – PostIndustria

Url: https://postindustria.com/ai-in-drug-discovery-top-cases-transforming-the-industry-machine-learning/

Highlights: It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases. In another study, an artificial neural network proved useful in predicting the antimicrobial properties of various molecules, which can make drug discovery faster, cheaper, and more reliable.A study by Daniil Polykovskiy and his team investigated the ability of AI to predict the activity of various synthesized molecules. It showed that AI can possibly improve the accuracy of the ​​drug screening process to make it more reliable. It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases.A list of examples proving the success of AI in drug discovery would be incomplete without mentioning this study by Limeng Pu et al. They created the artificial intelligence software eToxPred to predict toxicity levels of various synthetic and biological compounds to reduce the need for clinical trials. Their AI model accurately predicted toxic properties in more than 72% of cases, with an overall error rate of only 4%, which was accurate enough to potentially reduce the use of clinical trials.

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [7]

Title: Artificial intelligence in drug discovery and development - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7577280/

Highlights: Involvement of AI in the development of a pharmaceutical product from the bench to the bedside can be imagined given that it can aid rational drug design [16]; assist in decision making; determine the right therapy for a patient, including personalized medicines; and manage the clinical data generated and use it for future drug development [17]. E-VAI is an analytical and decision-making AI platform developed by Eularis, which uses ML algorithms along with an easy-to-use user interface to create analytical roadmaps based on competitors, key stakeholders, and currently held market share to predThis review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period.Applications of artificial intelligence (AI) in different subfields of the pharmaceutical industry, from drug discovery to pharmaceutical product management. The vast chemical space, comprising >1060 molecules, fosters the development of a large number of drug molecules [19]. However, the lack of advanced technologies limits the drug development process, making it a time-consuming and expensive task, which can be addressed by using AI [15]. AI can recognize hit and lead compounds, and provide a quicker validation of the drug target and optimization of the drug structure design 19, 20.This review highlights the impactful use of AI in diverse areas of the pharmaceutical sectors viz., drug discovery and development, drug repurposing, improving pharmaceutical productivity, clinical trials, etc. to name a few, thus reducing the human workload as well as achieving targets in a short period. Crosstalk on the tools and techniques utilized in enforcing AI, ongoing challenges, and ways to overcome them, along with the future of AI in the pharmaceutical industry, is also discussed.

Reference [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.

Reference [11]

Title: Artificial intelligence in pharmaceutical regulatory affairs - PubMed

Url: https://pubmed.ncbi.nlm.nih.gov/37442291/

Highlights: AI can be integrated to simplify the complexity of pharmaceutical regulatory affairs. AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management.AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management. AI creates process links and reduces complexity, resulting in a more efficient management system. Human-AI interaction opens up new opportunities in regulatory affairs. This article explores the potential role of AI in pharmaceutical regulatory affairs.

Reference [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.

Reference [11]

Title: Artificial intelligence in pharmaceutical regulatory affairs - PubMed

Url: https://pubmed.ncbi.nlm.nih.gov/37442291/

Highlights: AI can be integrated to simplify the complexity of pharmaceutical regulatory affairs. AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management.AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management. AI creates process links and reduces complexity, resulting in a more efficient management system. Human-AI interaction opens up new opportunities in regulatory affairs. This article explores the potential role of AI in pharmaceutical regulatory affairs.

Reference [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.

Reference [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.

Reference [13]

Title: How FDA Regulates Artificial Intelligence in Medical Products | The Pew Charitable Trusts

Url: https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2021/08/how-fda-regulates-artificial-intelligence-in-medical-products

Highlights: It will take a collective effort by FDA, Congress, technology developers, and the health care industry to ensure the safety and effectiveness of AI-enabled technology. AI refers to the ability of a machine to perform a task that mimics human behavior, including problem-solving and learning.3 It can be used for a range of purposes, including automating tasks, identifying patterns in data, and synthesizing multiple sources of information. In health care, AI technologies are already used in fields that rely on image analysis, such as radiology and ophthalmology, and in products that process and analyze data from wearable sensors to detect diseases or infer the onset of other health conditions.4Especially as the use of AI products in health care proliferates, FDA and other stakeholders will need to develop clear guidelines on the clinical evidence necessary to demonstrate the safety and effectiveness of such products and the extent to which product labels need to specify limitations on their performance and generalizability. As part of this effort, the agency could consider requiring developers to provide public information about the data used to validate and test AI devices so that end users can better understand their benefits and risks. FDA’s recent SaMD Action Plan is a good step forward, but the agency will still need to clarify other key issues, including:The regulatory framework governing these tools is complex. FDA regulates some—but not all—AI-enabled products used in health care, and the agency plays an important role in ensuring the safety and effectiveness of those products under its jurisdiction. The agency is currently considering how to adapt its review process for AI-enabled medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways that are difficult to foresee.2

Reference [5]

Title: Leveraging AI for drug development through regulatory intelligence | ZS

Url: https://www.zs.com/insights/technology-regulatory-intelligence-accelerate-drug-development

Highlights: Currently, the regulatory intelligence process is almost entirely manual, but we believe that technology-aided regulatory intelligence tools could greatly improve the drug development process. But to make progress here, pharma companies and regulatory affairs subject matter experts (SMEs) need to be aware of how existing technologies could help them and remain open to change. The journey from study completion to regulatory approval and market authorization in all the countries a company targets for launch is a long one. But there are numerous ways to speed this up—from selecting the appropriate drug approval pathway to utilizing different accelerated trial design options.If pharma companies are willing to put thought and effort into seeking out technology solutions for these processes, we believe that digitizing these steps could accelerate drug development timelines by 10 months. They can also ensure that companies make the most optimal regulatory strategy decisions and use the most appropriate regulatory pathway. But technology by itself can’t succeed without the processes and people trained to work alongside it.

Reference [14]

Title: Artificial intelligence in medicine regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation

Highlights: The International Coalition of Medicines Regulatory Authorities (ICMRA) sets out recommendations to help regulators to address the challenges that the use of artificial intelligence (AI) poses for global medicines regulation, in a report published today.

Reference [13]

Title: How FDA Regulates Artificial Intelligence in Medical Products | The Pew Charitable Trusts

Url: https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2021/08/how-fda-regulates-artificial-intelligence-in-medical-products

Highlights: It will take a collective effort by FDA, Congress, technology developers, and the health care industry to ensure the safety and effectiveness of AI-enabled technology. AI refers to the ability of a machine to perform a task that mimics human behavior, including problem-solving and learning.3 It can be used for a range of purposes, including automating tasks, identifying patterns in data, and synthesizing multiple sources of information. In health care, AI technologies are already used in fields that rely on image analysis, such as radiology and ophthalmology, and in products that process and analyze data from wearable sensors to detect diseases or infer the onset of other health conditions.4Especially as the use of AI products in health care proliferates, FDA and other stakeholders will need to develop clear guidelines on the clinical evidence necessary to demonstrate the safety and effectiveness of such products and the extent to which product labels need to specify limitations on their performance and generalizability. As part of this effort, the agency could consider requiring developers to provide public information about the data used to validate and test AI devices so that end users can better understand their benefits and risks. FDA’s recent SaMD Action Plan is a good step forward, but the agency will still need to clarify other key issues, including:The regulatory framework governing these tools is complex. FDA regulates some—but not all—AI-enabled products used in health care, and the agency plays an important role in ensuring the safety and effectiveness of those products under its jurisdiction. The agency is currently considering how to adapt its review process for AI-enabled medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways that are difficult to foresee.2

Reference [15]

Title: Artificial intelligence workplan to guide use of AI in medicines regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-workplan-guide-use-ai-medicines-regulation

Highlights: EMA and the Heads of Medicines Agencies (HMAs) have published an artificial intelligence (AI) workplan to 2028, setting out a collaborative and coordinated strategy to maximise the benefits of AI to stakeholders while managing the risks. ... The workplan will help the European medicines regulatory network (EMRN) to embrace the opportunities of AI for personal productivity, automating processes and systems, increasing insights into data and supporting more robust decision-making to benefit public and animal health.The workplan was adopted by EMA’s Management Board at its December meeting. The field of AI is developing swiftly. Pharmaceutical companies increasingly use AI-powered tools in research, development and monitoring of medicines. National competent authorities are responding to the new opportunities and challenges by starting to use and develop AI tools.

Reference [10]

Title: AI in Drug Discovery: Top Cases Transforming the Industry – PostIndustria

Url: https://postindustria.com/ai-in-drug-discovery-top-cases-transforming-the-industry-machine-learning/

Highlights: It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases. In another study, an artificial neural network proved useful in predicting the antimicrobial properties of various molecules, which can make drug discovery faster, cheaper, and more reliable.A study by Daniil Polykovskiy and his team investigated the ability of AI to predict the activity of various synthesized molecules. It showed that AI can possibly improve the accuracy of the ​​drug screening process to make it more reliable. It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases.A list of examples proving the success of AI in drug discovery would be incomplete without mentioning this study by Limeng Pu et al. They created the artificial intelligence software eToxPred to predict toxicity levels of various synthetic and biological compounds to reduce the need for clinical trials. Their AI model accurately predicted toxic properties in more than 72% of cases, with an overall error rate of only 4%, which was accurate enough to potentially reduce the use of clinical trials.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.

Reference [16]

Title: Leveraging AI in Regulatory Affairs: A Pharmaceutical Game-Changer

Url: https://www.linkedin.com/pulse/leveraging-ai-regulatory-affairs-pharmaceutical-yossi-shmeterer

Highlights: Pharmaceutical companies often operate in multiple jurisdictions, each with its own regulations. An AI system trained to understand different regulatory environments can help firms navigate these complexities efficiently. It can also translate and adapt documents to meet region-specific requirements, making global operations less cumbersome.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [17]

Title: 2022 in review: Regulation starts to catch up with AI in pharma - Pharmaceutical Technology

Url: https://www.pharmaceutical-technology.com/features/2022-in-review-regulation-starts-to-catch-up-with-ai-in-pharma/

Highlights: However, despite increasing AI adoption, there has reportedly been no substantial increase in AI-related risk mitigation for companies that used AI in at least one function since 2019. This could spell high-stake consequences for consumers whose privacy and safety could be at risk if AI models are not regulated. Moreover, concerns about fairness, bias, and accountability of AI systems have increasingly been at the forefront of the industry.

Reference [18]

Title: Controlling the pharma machine: EMA conveys thoughts on AI use in industry - Pharmaceutical Technology

Url: https://www.pharmaceutical-technology.com/news/controlling-pharma-machine-ema-ai/

Highlights: AI brings exciting opportunities to generate new insights and improve processes. To embrace them fully, we will need to be prepared for the regulatory challenges presented by this quickly evolving ecosystem.” · EMA’s Head of Data Analytics and Methods and BDSG co-chair Peter Arlett said: “With this paper, we are opening a dialogue with developers, academics, and other regulators, to discuss ways forward, ensuring that the full potential of these innovations can be realised for the benefit of patients’ and animal health.”EMA’s Head of Data Analytics and Methods and BDSG co-chair Peter Arlett said: “With this paper, we are opening a dialogue with developers, academics, and other regulators, to discuss ways forward, ensuring that the full potential of these innovations can be realised for the benefit of patients’ and animal health.” · Give your business an edge with our leading industry insights. ... The gold standard of business intelligence. ... Give your business an edge with our leading industry insights.

Reference [6]

Title: Artificial Intelligence in Pharmaceutical Technology and Drug Delivery Design - PMC

Url: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10385763/

Highlights: AI involves the use of advanced tools and software to achieve human-like capabilities. Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Such innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain.Supply Chain Optimization: AI is applied to optimize pharmaceutical supply chains, ensuring efficient manufacturing, inventory management, and distribution. AI algorithms can predict demand, optimize production schedules, and enhance quality control processes, contributing to more streamlined and cost-effective operations.The pharmaceutical industry is a critical field that plays a vital role in saving lives. It operates based on continuous innovation and the adoption of new technologies to address global healthcare challenges and respond to medical emergencies, such as the recent pandemic [1]. In the pharmaceutical industry, innovation is typically predicated on extensive research and development across various domains, including but not limited to manufacturing technology, packaging considerations, and customer-oriented marketing strategies [2]. Novel pharmaceutical innovations are range from small drug molecules to biologics, with a preference for better stability with high potency to fulfil unmet needs to treat diseases.Depicts a possible artificial intelligence (AI) solution to the pharmaceutical industry’s challenges: acquiring a proficient workforce is a prerequisite in all sectors to leverage their expertise, proficiency, and aptitude in product innovation. The second pertains to supply chain disruption and clinical trial experimentation challenges. The incidence of cyberattacks is on the rise, with data breaches and security emerging as significant concerns for the industry. The primary impact of the pandemic is receding, but it still has some influence on clinical trials.Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSARSuch innovation has helped in many sectors, such as the pharmaceutical industry, especially in the product development phase over the past few years. The implementation of these technological innovations can save time, money, and resources required for manufacturing and proper distribution to end customers through the supply chain. It also provides a better platform to understand the impact of process parameters on the formulation and manufacturing of products.Remarkable advancements in AI technology and machine learning present a transformative opportunity in the drug discovery, formulation, and testing of pharmaceutical dosage forms. By utilizing AI algorithms that analyze extensive biological data, including genomics and proteomics, researchers can identify disease-associated targets and predict their interactions with potential drug candidates. This enables a more efficient and targeted approach to drug discovery, thereby increasing the likelihood of successful drug approvals. Furthermore, AI can contribute to reducing development costs by optimizing research and development processes.This review provides an overview of various AI-based approaches utilized in pharmaceutical technology, highlighting their benefits and drawbacks. Nevertheless, the continued investment in and exploration of AI in the pharmaceutical industry offer exciting prospects for enhancing drug development processes and patient care. Keywords: artificial intelligence (AI), machine learning, drug discovery, formulation, dosage form testing, pharmacokinetics, pharmacodynamics, PBPK, QSAR

Reference [11]

Title: Artificial intelligence in pharmaceutical regulatory affairs - PubMed

Url: https://pubmed.ncbi.nlm.nih.gov/37442291/

Highlights: AI can be integrated to simplify the complexity of pharmaceutical regulatory affairs. AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management.AI tools can be applied to automate regulatory processes such as administrative work, dossier filling, data extraction, auditing, the implementation of regulations, and quality management. AI creates process links and reduces complexity, resulting in a more efficient management system. Human-AI interaction opens up new opportunities in regulatory affairs. This article explores the potential role of AI in pharmaceutical regulatory affairs.

Reference [4]

Title: Artificial Intelligence (AI) in Pharma: How to Use It in 2024

Url: https://viseven.com/artificial-intelligence-in-pharma-industry/

Highlights: AI algorithms and machine learning models have a significant impact on the biotech industry. From life-saving drugs discovery, development, and production to clinical trials, communication, and drug target identification — AI pharmaceutical is a definite game-changer. According to The McKinsey Global Institute’s research, the influence of AI and machine learning on the pharma market generated around $100B across the US healthcare system in 2021.Also, artificial intelligence pharmaceutical automation allows your company to perform predictive maintenance and quality control of drug combinations. There’s no sign of this trend slowing down — on the contrary, about 50 percent of global healthcare companies plan to implement AI strategies and broadly adopt the technology by 2025.

Reference [1]

Title: Artificial intelligence in pharmaceutical regulatory affairs - ScienceDirect

Url: https://www.sciencedirect.com/science/article/abs/pii/S1359644623002167

Highlights: Artificial intelligence (AI) is rapidly transforming the pharmaceutical industry, and regulatory affairs is no exception. The use of AI in pharmaceutical regulatory affairs is still in its early stages, but it has the potential to revolutionize the industry. Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval.In turn, regulatory authorities approve products based on data and documents submitted at all stages from drug discovery to product development.1 Automation could help speed up this process by reducing the time required for collecting, segregating, and standardizing data from records, as well as by reducing the requirement for human involvement in the documentation process.1, 2 · AI is a software or computer program developed to perform tasks that require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.2 It is a multidisciplinary fiTraditional regulatory processes are slow, and AI has already been applied in areas other than regulatory affairs in the pharmaceutical industry. Combining AI with human intelligence has great potential and maximizes the time available for strategic planning of regulatory approvals.Integration of AI into regulatory affairs will help with staying up-to-date on the latest industry trends and learning about the potential benefits of AI for drug discovery, as well as its regulatory approval. AI can help drug discovery professionals bring new drugs to market faster and more efficiently by automating tasks, improving decision-making, and identifying new opportunities. Regulatory affairs refers to the teams or functions within pharmaceutical companies that provide a link to regulatory authorities, of the host country or elsewhere, who work to gain product approval according to current regulations.

Reference [14]

Title: Artificial intelligence in medicine regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation

Highlights: The International Coalition of Medicines Regulatory Authorities (ICMRA) sets out recommendations to help regulators to address the challenges that the use of artificial intelligence (AI) poses for global medicines regulation, in a report published today.

Reference [14]

Title: Artificial intelligence in medicine regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation

Highlights: The International Coalition of Medicines Regulatory Authorities (ICMRA) sets out recommendations to help regulators to address the challenges that the use of artificial intelligence (AI) poses for global medicines regulation, in a report published today.

Reference [19]

Title: European Industrial Pharmacists Group (EIPG) EMA’s reflection paper on AI in the pharmaceutical lifecycle

Url: https://eipg.eu/ema-reflection-paper-on-ai-in-the-pharmaceutical-lifecycle/

Highlights: The initiative aims to identify the aspects of AI/ML use that would fall within the remit of EMA or of the single National Competent Authorities (NCAs) in charge of the dossiers’ assessment. The document is part of the effort to improve the European Medicines Regulatory Network’s capability in data-driven regulation set forth by the joint HMA-EMA Big Data Steering Group (BDSG), and it has been developed in coordination with EMA’s CHMP and CVMP committees.

Reference [14]

Title: Artificial intelligence in medicine regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-medicine-regulation

Highlights: The International Coalition of Medicines Regulatory Authorities (ICMRA) sets out recommendations to help regulators to address the challenges that the use of artificial intelligence (AI) poses for global medicines regulation, in a report published today.

Reference [13]

Title: How FDA Regulates Artificial Intelligence in Medical Products | The Pew Charitable Trusts

Url: https://www.pewtrusts.org/en/research-and-analysis/issue-briefs/2021/08/how-fda-regulates-artificial-intelligence-in-medical-products

Highlights: It will take a collective effort by FDA, Congress, technology developers, and the health care industry to ensure the safety and effectiveness of AI-enabled technology. AI refers to the ability of a machine to perform a task that mimics human behavior, including problem-solving and learning.3 It can be used for a range of purposes, including automating tasks, identifying patterns in data, and synthesizing multiple sources of information. In health care, AI technologies are already used in fields that rely on image analysis, such as radiology and ophthalmology, and in products that process and analyze data from wearable sensors to detect diseases or infer the onset of other health conditions.4Especially as the use of AI products in health care proliferates, FDA and other stakeholders will need to develop clear guidelines on the clinical evidence necessary to demonstrate the safety and effectiveness of such products and the extent to which product labels need to specify limitations on their performance and generalizability. As part of this effort, the agency could consider requiring developers to provide public information about the data used to validate and test AI devices so that end users can better understand their benefits and risks. FDA’s recent SaMD Action Plan is a good step forward, but the agency will still need to clarify other key issues, including:The regulatory framework governing these tools is complex. FDA regulates some—but not all—AI-enabled products used in health care, and the agency plays an important role in ensuring the safety and effectiveness of those products under its jurisdiction. The agency is currently considering how to adapt its review process for AI-enabled medical devices that have the ability to evolve rapidly in response to new data, sometimes in ways that are difficult to foresee.2

Reference [15]

Title: Artificial intelligence workplan to guide use of AI in medicines regulation | European Medicines Agency

Url: https://www.ema.europa.eu/en/news/artificial-intelligence-workplan-guide-use-ai-medicines-regulation

Highlights: EMA and the Heads of Medicines Agencies (HMAs) have published an artificial intelligence (AI) workplan to 2028, setting out a collaborative and coordinated strategy to maximise the benefits of AI to stakeholders while managing the risks. ... The workplan will help the European medicines regulatory network (EMRN) to embrace the opportunities of AI for personal productivity, automating processes and systems, increasing insights into data and supporting more robust decision-making to benefit public and animal health.The workplan was adopted by EMA’s Management Board at its December meeting. The field of AI is developing swiftly. Pharmaceutical companies increasingly use AI-powered tools in research, development and monitoring of medicines. National competent authorities are responding to the new opportunities and challenges by starting to use and develop AI tools.

Reference [10]

Title: AI in Drug Discovery: Top Cases Transforming the Industry – PostIndustria

Url: https://postindustria.com/ai-in-drug-discovery-top-cases-transforming-the-industry-machine-learning/

Highlights: It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases. In another study, an artificial neural network proved useful in predicting the antimicrobial properties of various molecules, which can make drug discovery faster, cheaper, and more reliable.A study by Daniil Polykovskiy and his team investigated the ability of AI to predict the activity of various synthesized molecules. It showed that AI can possibly improve the accuracy of the ​​drug screening process to make it more reliable. It also uncovered vital information about the target molecule, demonstrating that AI can be integrated into automated drug discovery pipelines to generate a large set of baseline drug hypotheses for a variety of diseases.A list of examples proving the success of AI in drug discovery would be incomplete without mentioning this study by Limeng Pu et al. They created the artificial intelligence software eToxPred to predict toxicity levels of various synthetic and biological compounds to reduce the need for clinical trials. Their AI model accurately predicted toxic properties in more than 72% of cases, with an overall error rate of only 4%, which was accurate enough to potentially reduce the use of clinical trials.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.

Reference [16]

Title: Leveraging AI in Regulatory Affairs: A Pharmaceutical Game-Changer

Url: https://www.linkedin.com/pulse/leveraging-ai-regulatory-affairs-pharmaceutical-yossi-shmeterer

Highlights: Pharmaceutical companies often operate in multiple jurisdictions, each with its own regulations. An AI system trained to understand different regulatory environments can help firms navigate these complexities efficiently. It can also translate and adapt documents to meet region-specific requirements, making global operations less cumbersome.

Reference [12]

Title: Pharmaceuticals | Free Full-Text | The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies

Url: https://www.mdpi.com/1424-8247/16/6/891

Highlights: Another important application of AI in drug discovery is the identification of drug–drug interactions that take place when several drugs are combined for the same or different diseases in the same patient, resulting in altered effects or adverse reactions. This issue can be identified by AI-based approaches by analyzing large datasets of known drug interactions and recognizing the patterns and trends. This has been recently addressed by an ML algorithm used to accurately predict the interactions of novel drug pairs [18]. The role of AI to identify possible drug–drug interactions in the context of personalized medicine is also relevant, enabling the development of custom-made treatment plans that minimize the risk of adverse reactions.The role of collaboration between AI researchers and pharmaceutical scientists is crucial in the development of innovative and effective treatments for various diseases. By combining their expertise and knowledge, they can create powerful algorithms and machine-learning models intended to predict the efficacy of potential drug candidates and speed up the drug discovery process. This collaboration can also help improve the accuracy and efficiency of clinical trials, as AI algorithms can be used to analyze the data collected during these trials to identify trends and the potential adverse effects of the drugs being tested.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process.The previous examples from the literature demonstrate that the use of AI in pharmaceutical research offers the ability to improve the prediction of the efficacy and toxicity of potential drug compounds. This can enable the development of more effective and safer medications and accelerate the drug discovery process. Another key application of AI in drug discovery is the design of novel compounds with specific properties and activities.