Esmaeilpour Donya, Hamblin Michael R, Cheng Jianlin, Khosravi Arezoo, Liu Jian, Zarepour Atefeh, Zarrabi Ali, Sillanpää Mika, Nazarzadeh Zare Ehsan, Shen Jianliang, Karimi-Maleh Hassan
Center for Nanotechnology in Drug Delivery, School of Pharmacy, Shiraz University of Medical Science, Shiraz, 71345-1583, Iran.
Distinguished Visiting Professor, Laser Research Center, University of Johannesburg, South Africa.
Bioact Mater. 2026 Jan 30;60:425-455. doi: 10.1016/j.bioactmat.2026.01.036. eCollection 2026 Jun.
The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines.
人工智能、蛋白质工程和可持续纳米医学的整合正在推动治疗诊断学的范式转变，实现高精度疾病诊断和靶向治疗。人工智能驱动的方法，包括机器学习和深度学习，有助于快速分析复杂的生物和化学数据集，加速蛋白质结构预测、分子对接和构效关系建模。这些能力支持合理设计具有更高特异性、治疗效果和安全性的蛋白质和肽，同时实现针对个体分子特征的个性化治疗策略。与此同时，可持续纳米医学专注于开发可生物降解、生物相容且对环境无害的纳米材料，以提高药物的生物利用度、稳定性和控释性。人工智能辅助优化通过平衡治疗性能与安全性和环境影响，进一步完善纳米载体设计。能够实时监测、自适应药物释放并降解为无毒副产物的先进智能纳米载体代表了相对于传统静态系统的重大进步。治疗诊断范式已成为精准医学的核心，尤其是在肿瘤学领域，特别是人工智能设计的纳米平台能够将成像剂和治疗药物靶向递送至肿瘤，同时允许持续的治疗监测并将脱靶效应降至最低。在神经、感染和心血管疾病方面的新兴应用进一步凸显了这种方法广泛的临床潜力。因此，本综述总结了人工智能驱动的蛋白质设计策略、可持续纳米载体工程及其在下一代治疗诊断系统中的融合，批判性地讨论了开发安全、可扩展且临床适用的智能纳米药物所需的机制见解、转化挑战和设计原则。
