Abstract：
Viral-like particles (VLPs) represent versatile nanoscale structures mimicking the morphology and antigenic characteristics of viruses, devoid of genetic material, making them promising candidates for various biomedical applications. The integration of artificial intelligence (AI) into VLP research has catalyzed significant advancements in understanding, production, and therapeutic applications of these nanostructures. This comprehensive review explores the collaborative utilization of AI tools, computational methodologies, and state-of-the-art technologies within the VLP domain. AI\'s involvement in bioinformatics facilitates sequencing and structure prediction, unraveling genetic intricacies and three-dimensional configurations of VLPs. Furthermore, AI-enabled drug discovery enables virtual screening, demonstrating promise in identifying compounds to inhibit VLP activity. In VLP production, AI optimizes processes by providing strategies for culture conditions, nutrient concentrations, and growth kinetics. AI\'s utilization in image analysis and electron microscopy expedites VLP recognition and quantification. Moreover, network analysis of protein-protein interactions through AI tools offers an understanding of VLP interactions. The integration of multi-omics data via AI analytics provides a comprehensive view of VLP behavior. Predictive modeling utilizing machine learning algorithms aids in forecasting VLP stability, guiding optimization efforts. Literature mining facilitated by text mining algorithms assists in summarizing information from the VLP knowledge corpus. Additionally, AI\'s role in laboratory automation enhances experimental efficiency. Addressing data security concerns, AI ensures the protection of sensitive information in the digital era of VLP research. This review serves as a roadmap, providing insights into AI\'s current and future applications in VLP research, thereby guiding innovative directions in medicine and beyond.
摘要：
病毒样颗粒（VLP）代表了模拟病毒形态和抗原特性的多功能纳米级结构，缺乏遗传物质，使它们成为各种生物医学应用的有前途的候选人。将人工智能（AI）集成到VLP研究中，在理解方面取得了重大进展，生产,和这些纳米结构的治疗应用。这篇全面的综述探讨了人工智能工具的协作利用，计算方法，以及VLP域内最先进的技术。AI参与生物信息学有助于测序和结构预测，解开VLP的遗传复杂性和三维构型。此外，支持AI的药物发现可以实现虚拟筛选，证明了在鉴定抑制VLP活性的化合物方面的希望。在VLP生产中，AI通过提供培养条件的策略来优化流程，营养素浓度，和生长动力学。人工智能在图像分析和电子显微镜中的应用加快了VLP识别和量化。此外，通过AI工具对蛋白质-蛋白质相互作用的网络分析提供了对VLP相互作用的理解。通过AI分析集成多组学数据提供了VLP行为的全面视图。利用机器学习算法的预测建模有助于预测VLP稳定性，指导优化工作。文本挖掘算法促进的文献挖掘有助于从VLP知识语料库中总结信息。此外,人工智能在实验室自动化中的作用提高了实验效率。解决数据安全问题，AI确保在VLP研究的数字时代保护敏感信息。这次审查是一个路线图，提供对AI在VLP研究中的当前和未来应用的见解，从而指导医学及其他领域的创新方向。
