Computational Drug Design for SARS-CoV-2 Inhibition

Computational drug design for SARS-CoV-2 inhibition has emerged as a keystone in the rapid discovery of antiviral leads, drawing on advances in structural biology, cheminformatics and artificial intelligence. By integrating high-resolution crystallographic and cryo-EM data with in silico screening, researchers can interrogate essential viral proteins such as the main protease (Mpro), RNA-dependent RNA polymerase (RdRp), spike glycoprotein and accessory macrodomains. Structure-based approaches—including virtual screening, molecular docking and molecular dynamics simulations—allow the rapid evaluation of millions of compounds against defined binding pockets. Fragment-based design and ultra-large library docking have yielded novel scaffolds with micromolar affinity, while machine learning and deep-learning models accelerate repurposing of approved drugs by predicting drug–target interactions from chemical and protein sequence data. Iterative cycles of design, synthesis and structural validation have delivered selective, cell-permeable inhibitors, demonstrating the potential for precision targeting of viral enzyme machineries. These computational pipelines have underpinned global efforts to generate leads within months rather than years, offering a blueprint for rapid response to emerging pathogens and guiding combination strategies that may reduce resistance and improve clinical outcomes.

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Computational Drug Design for SARS-CoV-2 Inhibition publication trend

The graph below shows the total number of articles in computational drug design for sars-cov-2 inhibition across all publications each year (not limited to Nature Index journals).

Technical terms

Virtual screening: Computational evaluation of large compound libraries against a target structure to identify potential binders.

Molecular docking: In silico prediction of the preferred orientation and binding affinity of a small molecule within a protein’s active site.

Molecular dynamics simulation: Time-resolved computational modelling that assesses the stability and conformational changes of protein–ligand complexes.

Fragment-based design: Strategy that assembles small chemical fragments into larger inhibitors by exploiting fragment binding modes in the target site.

Drug–target interaction model: Machine learning or deep-learning algorithm that predicts affinity between compounds and protein targets from structural or sequence data.

Macrodomain: A conserved viral protein fold involved in evasion of host immunity, presenting a novel pharmacological target in SARS-CoV-2.

References

1. Iterative computational design and crystallographic screening identifies potent inhibitors targeting the Nsp3 macrodomain of SARS-CoV-2. Proceedings of the National Academy of Sciences of the United States of America (2023).
2. A suitable drug structure for interaction with SARS‐CoV‐2 main protease between boceprevir, masitinib and rupintrivir; a molecular dynamics study. Arabian Journal of Chemistry (2023).
3. Predicting commercially available antiviral drugs that may act on the novel coronavirus (SARS-CoV-2) through a drug-target interaction deep learning model. Computational and Structural Biotechnology Journal (2020).