Abughalia Abedalrahman, Flynn Mairead, Clarke Paul F A, Fayne Darren, Gobbo Oliviero L
School of Pharmacy and Pharmaceutical Sciences, Trinity College Dublin, D02 PN40 Dublin, Ireland.
Molecular Design Group, School of Chemical Sciences, Dublin City University, D09 V209 Dublin, Ireland.
Nanomaterials (Basel). 2025 Sep 3;15(17):1354. doi: 10.3390/nano15171354.
Nano-based drug delivery systems present a promising approach to improve the efficacy and safety of therapeutics by enabling targeted drug transport and controlled release. In parallel, computational approaches-particularly Molecular Dynamics (MD) simulations and Artificial Intelligence (AI)-have emerged as transformative tools to accelerate nanocarrier design and optimise their properties. MD simulations provide atomic-to-mesoscale insights into nanoparticle interactions with biological membranes, elucidating how factors such as surface charge density, ligand functionalisation and nanoparticle size affect cellular uptake and stability. Complementing MD simulations, AI-driven models accelerate the discovery of lipid-based nanoparticle formulations by analysing vast chemical datasets and predicting optimal structures for gene delivery and vaccine development. By harnessing these computational approaches, researchers can rapidly refine nanoparticle composition to improve biocompatibility, reduce toxicity and achieve more precise drug targeting. This review synthesises key advances in MD simulations and AI for two leading nanoparticle platforms (gold and lipid nanoparticles) and highlights their role in enhancing therapeutic performance. We evaluate how in silico models guide experimental validation, inform rational design strategies and ultimately streamline the transition from bench to bedside. Finally, we address key challenges such as data scarcity and complex in vivo dynamics and propose future directions for integrating computational insights into next generation nanodelivery systems.
基于纳米的药物递送系统为改善治疗效果和安全性提供了一种有前景的方法，它能够实现靶向药物运输和控释。与此同时，计算方法——特别是分子动力学（MD）模拟和人工智能（AI）——已成为加速纳米载体设计和优化其性能的变革性工具。MD模拟提供了从原子尺度到介观尺度的对纳米颗粒与生物膜相互作用的见解，阐明了诸如表面电荷密度、配体功能化和纳米颗粒大小等因素如何影响细胞摄取和稳定性。作为MD模拟的补充，人工智能驱动的模型通过分析大量化学数据集并预测用于基因递送和疫苗开发的最佳结构，加速了基于脂质的纳米颗粒制剂的发现。通过利用这些计算方法，研究人员可以快速优化纳米颗粒组成，以提高生物相容性、降低毒性并实现更精确的药物靶向。本综述综合了MD模拟和AI在两个主要纳米颗粒平台（金纳米颗粒和脂质纳米颗粒）方面的关键进展，并强调了它们在提高治疗性能中的作用。我们评估了计算机模型如何指导实验验证、为合理设计策略提供信息，并最终简化从实验台到临床应用的转化。最后，我们讨论了数据稀缺和复杂的体内动力学等关键挑战，并提出了将计算见解整合到下一代纳米递送系统中的未来方向。
