Computer-aided nanodrug discovery: recent progress and future prospects,Chemical Society Reviews

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Computer-aided nanodrug discovery: recent progress and future prospects
Chemical Society Reviews ( IF 40.4 ) Pub Date : 2024-08-16 , DOI: 10.1039/d3cs00575e
Jia-Jia Zheng ₁ , Qiao-Zhi Li ₁ , Zhenzhen Wang ₁ , Xiaoli Wang _{1,

2} , Yuliang Zhao ₁ , Xingfa Gao ₁

Affiliation

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 in silico 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.

中文翻译：

计算机辅助纳米药物发现：最新进展和未来前景

纳米药物利用纳米材料预防和治疗疾病，自 20 世纪 90 年代最初概念化以来，引起了人们极大的兴趣。为了克服传统药物的局限性，例如靶向功效低、剂量和毒性高以及潜在的耐药性，人们已经做出了大量努力来开发纳米药物。尽管纳米药物发现取得了重大进展，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来说尤其如此，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机方法克服这一挑战成为可能，该方法提供了对纳米药物与其理化性质相关的医学功能的全面了解。此外，机器学习技术已广泛应用于纳米药物研究，显着加速了对生物纳米相互作用的理解和纳米药物的开发。本综述将总结纳米药物发现的计算进展，重点关注关键界面相互作用（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功效。此外，本综述将讨论计算机辅助纳米药物发现的挑战和机遇，特别强调集成的“计算+机器学习+实验”策略，该策略有可能加速精密纳米药物的发现。

更新日期：2024-08-16

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