Laboratory of Theoretical and Computational Nanoscience, National Center for Nanoscience and Technology of China, Beijing 100190, China.
University of Chinese Academy of Sciences, No. 19A Yuquan Road, Beijing 100049, China.
Chem Soc Rev. 2024 Sep 16;53(18):9059-9132. doi: 10.1039/d3cs00575e.
Nanodrugs, which utilise nanomaterials in disease prevention and therapy, have attracted considerable interest since their initial conceptualisation in the 1990s. Substantial efforts have been made to develop nanodrugs for overcoming the limitations of conventional drugs, such as low targeting efficacy, high dosage and toxicity, and potential drug resistance. Despite the significant progress that has been made in nanodrug discovery, the precise design or screening of nanomaterials with desired biomedical functions prior to experimentation remains a significant challenge. This is particularly the case with regard to personalised precision nanodrugs, which require the simultaneous optimisation of the structures, compositions, and surface functionalities of nanodrugs. The development of powerful computer clusters and algorithms has made it possible to overcome this challenge through methods, which provide a comprehensive understanding of the medical functions of nanodrugs in relation to their physicochemical properties. In addition, machine learning techniques have been widely employed in nanodrug research, significantly accelerating the understanding of bio-nano interactions and the development of nanodrugs. This review will present a summary of the computational advances in nanodrug discovery, focusing on the understanding of how the key interfacial interactions, namely, surface adsorption, supramolecular recognition, surface catalysis, and chemical conversion, affect the therapeutic efficacy of nanodrugs. Furthermore, this review will discuss the challenges and opportunities in computer-aided nanodrug discovery, with particular emphasis on the integrated "computation + machine learning + experimentation" strategy that can potentially accelerate the discovery of precision nanodrugs.
纳米药物利用纳米材料进行疾病预防和治疗，自 20 世纪 90 年代首次提出以来，引起了广泛关注。为了克服传统药物的局限性，如靶向效率低、剂量高和毒性大以及潜在的耐药性，已经做出了大量努力来开发纳米药物。尽管在纳米药物发现方面取得了重大进展，但在实验前精确设计或筛选具有所需生物医学功能的纳米材料仍然是一个重大挑战。对于个性化精准纳米药物尤其如此，这需要同时优化纳米药物的结构、组成和表面功能。强大的计算机集群和算法的发展使得通过计算方法来克服这一挑战成为可能，这些方法可以全面了解纳米药物的医学功能与其物理化学性质之间的关系。此外，机器学习技术已广泛应用于纳米药物研究，极大地加速了对生物-纳米相互作用的理解和纳米药物的开发。本综述将介绍纳米药物发现中的计算进展，重点介绍关键界面相互作用（即表面吸附、超分子识别、表面催化和化学转化）如何影响纳米药物的治疗效果。此外，本文还将讨论计算机辅助纳米药物发现中的挑战和机遇，特别强调可能加速精准纳米药物发现的集成“计算+机器学习+实验”策略。
