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.

The coronavirus SARS-CoV-2 has caused a pandemic with > 550 millions of cases and > 6 millions of deaths worldwide. Medical management of COVID-19 relies on supportive care as no specific targeted therapies are available yet. Given its devastating effects on the economy and mental health, it is imperative to develop novel antivirals. An ideal candidate will be an agent that blocks the early events of viral attachment and cell entry, thereby preventing viral infection and spread. This work reports functionalized titanium dioxide (TiO₂)-based nanoparticles adsorbed with flavonoids that block SARS-CoV-2 entry and fusion. Using molecular docking analysis, two flavonoids were chosen for their specific binding to critical regions of the SARS-CoV-2 spike glycoprotein that interacts with the host cell angiotensin-converting enzyme-2 (ACE-2) receptor. These flavonoids were adsorbed onto TiO₂ functionalized nanoparticles (FTNP). This new nanoparticulate compound was assayed in vitro against two different coronaviruses; HCoV 229E and SARS-CoV-2, in both cases a clear antiviral effect was observed. Furthermore, using a reporter-based cell culture model, a potent antiviral activity is demonstrated. The adsorption of flavonoids to functionalized TiO₂ nanoparticles induces a ~ threefold increase of that activity. These studies also indicate that FTNP interferes with the SARS-CoV-2 spike, impairing the cell fusion mechanism. Key points/Highlights: • Unique TiO₂nanoparticles displaying flavonoid showed potent anti-SARS-CoV-2 activity. • The nanoparticles precisely targeting SARS-CoV-2 were quantitatively verified by cell infectivity in vitro. • Flavonoids on nanoparticles impair the interactions between the spike glycoprotein and ACE-2 receptor. Graphical

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