Dermawan Doni, Alotaiq Nasser
Department of Applied Biotechnology, Faculty of Chemistry, Warsaw University of Technology, 00-661 Warsaw, Poland.
Health Sciences Research Center (HSRC), Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13317, Saudi Arabia.
Pharmaceuticals (Basel). 2025 Jun 30;18(7):981. doi: 10.3390/ph18070981.
Artificial intelligence (AI) is transforming drug discovery and development by enhancing the speed and precision of identifying drug candidates and optimizing their efficacy. This review evaluates the application of AI in various stages of drug discovery, from hit identification to lead optimization, and its impact on clinical outcomes. The objective is to provide insights into the role of AI across therapeutic areas and assess its contributions to improving clinical trial efficiency and pharmaceutical outcomes. A systematic review followed PRISMA guidelines to analyze studies published between 2015 and 2025, focusing on AI in drug discovery and development. A comprehensive search was performed across multiple databases to identify studies employing AI techniques. The studies were categorized based on AI methods, clinical phase, and therapeutic area. The percentages of AI methods used, clinical phase stages, and the therapeutic regions were analyzed to identify trends. AI methods included machine learning (ML) at 40.9%, molecular modeling and simulation (MMS) at 20.7%, and deep learning (DL) at 10.3%. Oncology accounted for the majority of studies (72.8%), followed by dermatology (5.8%) and neurology (5.2%). In clinical phases, 39.3% of studies were in the preclinical stage, 23.1% in Clinical Phase I, and 11.0% in the transitional phase. Clinical outcome reporting was observed in 45% of studies, with 97% reporting industry partnerships. AI significantly enhances drug discovery and development, improving drug efficacy and clinical trial outcomes. Future work should focus on expanding AI applications into underrepresented therapeutic areas and refining models to handle complex biological systems.
人工智能（AI）正在通过提高识别候选药物的速度和精度以及优化其疗效来改变药物研发过程。本综述评估了AI在药物研发各个阶段（从苗头化合物识别到先导化合物优化）的应用及其对临床结果的影响。目的是深入了解AI在各个治疗领域中的作用，并评估其对提高临床试验效率和制药成果的贡献。一项系统综述遵循PRISMA指南，分析了2015年至2025年间发表的研究，重点关注AI在药物研发中的应用。在多个数据库中进行了全面搜索，以识别采用AI技术的研究。这些研究根据AI方法、临床阶段和治疗领域进行分类。分析了所使用的AI方法的百分比、临床阶段以及治疗领域，以确定趋势。AI方法包括机器学习（ML）占40.9%，分子建模与模拟（MMS）占20.7%，深度学习（DL）占10.3%。肿瘤学领域的研究占大多数（72.8%），其次是皮肤病学（5.8%）和神经病学（5.2%）。在临床阶段，39.3%的研究处于临床前阶段，23.1%处于临床I期，11.0%处于过渡阶段。45%的研究报告了临床结果，其中97%报告了与行业的合作关系。AI显著增强了药物研发，提高了药物疗效和临床试验结果。未来的工作应侧重于将AI应用扩展到研究较少的治疗领域，并改进模型以处理复杂的生物系统。
