Department of Microbiology & Immunology, College of Graduate Studies, Midwestern University, Downers Grove, IL 60515, USA.
Hinsdale Central High School, 5500 S Grant St, Hinsdale, IL 60521, USA.
Biomolecules. 2024 Jul 26;14(8):911. doi: 10.3390/biom14080911.
A highly critical event in a virus's life cycle is successfully entering a given host. This process begins when a viral glycoprotein interacts with a target cell receptor, which provides the molecular basis for target virus-host cell interactions for novel drug discovery. Over the years, extensive research has been carried out in the field of virus-host cell interaction, generating a massive number of genetic and molecular data sources. These datasets are an asset for predicting virus-host interactions at the molecular level using machine learning (ML), a subset of artificial intelligence (AI). In this direction, ML tools are now being applied to recognize patterns in these massive datasets to predict critical interactions between virus and host cells at the protein-protein and protein-sugar levels, as well as to perform transcriptional and translational analysis. On the other end, deep learning (DL) algorithms-a subfield of ML-can extract high-level features from very large datasets to recognize the hidden patterns within genomic sequences and images to develop models for rapid drug discovery predictions that address pathogenic viruses displaying heightened affinity for receptor docking and enhanced cell entry. ML and DL are pivotal forces, driving innovation with their ability to perform analysis of enormous datasets in a highly efficient, cost-effective, accurate, and high-throughput manner. This review focuses on the complexity of virus-host cell interactions at the molecular level in light of the current advances of ML and AI in viral pathogenesis to improve new treatments and prevention strategies.
病毒生命周期中的一个关键事件是成功进入给定的宿主。这个过程始于病毒糖蛋白与靶细胞受体相互作用，为新型药物发现提供了靶病毒-宿主细胞相互作用的分子基础。多年来，病毒-宿主细胞相互作用领域进行了广泛的研究，产生了大量的遗传和分子数据源。这些数据集是使用机器学习（ML）预测病毒-宿主相互作用的分子水平的资产，它是人工智能（AI）的一个子集。在这个方向上，ML 工具现在被应用于识别这些大规模数据集中的模式，以预测病毒和宿主细胞之间在蛋白质-蛋白质和蛋白质-糖水平上的关键相互作用，并进行转录和翻译分析。另一方面，深度学习（DL）算法——ML 的一个子领域——可以从非常大的数据集提取高级特征，以识别基因组序列和图像中的隐藏模式，从而开发用于快速药物发现预测的模型，以解决对受体对接显示出高度亲和力和增强细胞进入的致病病毒。ML 和 DL 是推动创新的关键力量，它们能够以高效、经济、准确和高通量的方式分析大量数据集。本综述重点讨论了分子水平上病毒-宿主细胞相互作用的复杂性，以及 ML 和 AI 在病毒发病机制方面的最新进展，以改进新的治疗和预防策略。
