Abdelsayed Mena
Lankenau Institute for Medical Research, 100 E Lancaster Ave., Penn Wynne, PA 19096, USA.
Int J Mol Sci. 2025 Jul 21;26(14):6996. doi: 10.3390/ijms26146996.
Polypharmacology, the rational design of small molecules that act on multiple therapeutic targets, offers a transformative approach to overcome biological redundancy, network compensation, and drug resistance. This review outlines the scientific rationale for polypharmacology, highlighting its success across oncology, neurodegeneration, metabolic disorders, and infectious diseases. Emphasis is placed on how polypharmacological agents can synergize therapeutic effects, reduce adverse events, and improve patient compliance compared to combination therapies. We also explore how computational methods-spanning ligand-based modeling, structure-based docking, network pharmacology, and systems biology-enable target selection and multi-target ligand prediction. Recent advances in artificial intelligence (AI), particularly deep learning, reinforcement learning, and generative models, have further accelerated the discovery and optimization of multi-target agents. These AI-driven platforms are capable of de novo design of dual and multi-target compounds, some of which have demonstrated biological efficacy in vitro. Finally, we discuss the integration of omics data, CRISPR functional screens, and pathway simulations in guiding multi-target design, as well as the challenges and limitations of current AI approaches. Looking ahead, AI-enabled polypharmacology is poised to become a cornerstone of next-generation drug discovery, with potential to deliver more effective therapies tailored to the complexity of human disease.
多药理学，即对作用于多个治疗靶点的小分子进行合理设计，为克服生物学冗余、网络补偿和耐药性提供了一种变革性方法。本综述概述了多药理学的科学原理，强调了其在肿瘤学、神经退行性疾病、代谢紊乱和传染病领域的成功。重点介绍了与联合疗法相比，多药理学药物如何协同治疗效果、减少不良事件并提高患者依从性。我们还探讨了计算方法——包括基于配体的建模、基于结构的对接、网络药理学和系统生物学——如何实现靶点选择和多靶点配体预测。人工智能（AI）的最新进展，特别是深度学习、强化学习和生成模型，进一步加速了多靶点药物的发现和优化。这些由人工智能驱动平台能够从头设计双靶点和多靶点化合物，其中一些已在体外证明了生物学疗效。最后，我们讨论了组学数据、CRISPR功能筛选和通路模拟在指导多靶点设计中的整合，以及当前人工智能方法的挑战和局限性。展望未来，人工智能驱动的多药理学有望成为下一代药物发现的基石，有可能提供更有效的疗法，以适应人类疾病的复杂性。
