The team began with the InterBioscreen (IBS) database, a curated collection of 69,075 natural products and natural product-like compounds. Such libraries are attractive starting points for antiviral drug discovery because natural scaffolds have been shaped by evolution to interact with proteins, often displaying chemical complexity and stereochemistry that synthetic combinatorial libraries struggle to match. Rather than docking every molecule against an arbitrary structure, the researchers employed a structure-guided strategy centered on the SARS-CoV-2 main protease, also known as Mpro or 3CL protease, a cysteine protease that cleaves the viral polyproteins at eleven conserved sites and is essential for viral replication. Because the enzyme has no close human homolog, inhibitors are less likely to interfere with host physiology, a property that has made Mpro one of the most thoroughly validated antiviral targets since the pandemic began.

High-throughput virtual screening of the full library produced a ranked list of candidate binders, which the team then subjected to more stringent re-docking to confirm that the apparent binding poses were reproducible and geometrically sensible. From this refined analysis, seven compounds emerged as hits. Each paired a high predicted binding affinity with specific hydrogen-bond interactions to residues lining the enzyme’s active site, the catalytic pocket where the virus cuts its own polyproteins. Notably, all seven compounds were new to this target and to the virus itself, meaning the screen had not simply rediscovered previously known inhibitors but surfaced an entirely fresh set of chemical scaffolds for evaluation.

Docking scores alone are a notoriously unreliable predictor of true binding, so the researchers advanced all seven hits to molecular dynamics (MD) simulations. These simulations solvate the protein-ligand complexes in explicit water and let the system evolve under physical force fields, revealing whether a docked pose survives the constant thermal jostling of a realistic molecular environment or falls apart within nanoseconds. The stability of each complex was further quantified using the MM-GBSA method, which estimates binding free energy by combining molecular mechanics interaction energies with continuum solvent models and can be averaged across many simulation snapshots to rank ligands more meaningfully than a single docking score. In parallel, the team predicted the ADME/Tox profiles, the absorption, distribution, metabolism, excretion, and toxicity characteristics, of each compound computationally, filtering out molecules likely to fail on pharmacokinetic grounds before any wet-lab work began.

After the dynamics-based triage, only three of the original seven compounds, STOCK1N-86169, STOCK1N-88750, and STOCK1N-65657, remained as high-potential candidates for Mpro inhibition. These three then entered the laboratory. The team ran a FRET-based Mpro inhibition assay, a standard enzymatic test in which a fluorescent peptide substrate is cleaved by the protease and inhibition is read as a preserved fluorescence signal, and an ELISA-based SARS-CoV-2 inhibition assay to probe antiviral activity more directly. The results were unambiguous: STOCK1N-86169 was the only potent inhibitor of the trio, inhibiting the enzyme with an IC50 of 3.8 micromolar, a concentration comfortably within the range expected of a chemical starting point for medicinal chemistry optimization.

Binding was independently confirmed by differential scanning calorimetry (DSC), a thermodynamic technique that measures the temperature at which a protein unfolds. Ligand binding typically stabilizes a protein against thermal denaturation, and the magnitude of the shift in melting temperature serves as a label-free readout of binding strength. When STOCK1N-86169 was bound to Mpro, the melting temperature of the complex rose by approximately 15 degrees Celsius relative to the unliganded protein, a substantial increase that corroborated the enzymatic assay and confirmed that the compound genuinely engages the protease rather than interfering artifactually with the assay chemistry.

The antiviral picture in cells was more nuanced. In cytotoxicity assays, STOCK1N-86169 showed a CC50 greater than 100 micromolar in HeLa cells, roughly 100 micromolar in Huh-7 human liver cells, and about 33.5 micromolar in Vero E6 kidney cells, indicating a moderate degree of cell-type variation in tolerance. In a Vero cell-based SARS-CoV-2 infection assay, the compound suppressed viral replication with an EC50 of approximately 7 micromolar, yielding a selective index, the ratio of cytotoxic to antiviral concentration, of around fivefold. A selective index of that size is modest by the standards of a clinical candidate, but for a first hit from an unoptimized natural product-like scaffold it represents a credible foundation, and the authors suggest the compound may be a promising candidate for further development as an effective inhibitor of SARS-CoV-2 Mpro.

The study sits within a broader and intensely active effort to expand the chemical arsenal against the main protease. Early in the pandemic, structures of Mpro published in 2020 enabled a global wave of inhibitor discovery, and since then the field has explored covalent peptidomimetics modeled on the enzyme’s substrate, non-covalent small molecules, and repurposed drugs. Plant-derived polyphenols such as baicalein, quercetin, and rutin have been reported as low-micromolar Mpro inhibitors, and open-science initiatives have iteratively refined screening hits into leads with improved drug-like properties. Against this backdrop, the new work is distinguished by its systematic use of a natural product-focused commercial library and by the completeness of its validation chain, which runs from virtual screening through dynamics, enzymology, thermodynamics, and live-virus assays, a chain that many purely computational studies never complete.

The methodological discipline on display is itself instructive. The pipeline incorporated several widely recommended safeguards: re-docking to filter initial screening artifacts, MD simulation to eliminate unstable poses, MM-GBSA energetics to rank complexes quantitatively, and ADME/Tox prediction to remove molecules with obvious pharmacokinetic liabilities. The design philosophy echoes lessons learned from decades of virtual screening, including the need to beware of pan-assay interference compounds, molecules that masquerade as inhibitors by aggregating or fluorescing nonspecifically. By demanding convergence between computational predictions and two independent experimental readouts, enzymatic inhibition and thermal stabilization, before declaring a winner, the Kolkata and Chandigarh team avoided many of the false positives that plague database-driven antiviral screens.

Whether STOCK1N-86169 ultimately advances toward a drug will depend on the usual gauntlet of lead optimization: improving potency, clarifying its mechanism of binding at atomic resolution, tuning its selectivity index upward, and establishing pharmacokinetic behavior in animals. Its novelty as a scaffold, unprecedented for this target and for the virus according to the authors, is an asset, since new chemotypes give medicinal chemists fresh territory for structural modification when resistance or tolerability problems arise with existing inhibitor classes. For now, the study stands as a template for rigorous antiviral hit discovery, showing that a carefully filtered virtual screen of a natural product database, when anchored to experimental verification at every stage, can still deliver genuinely new chemical matter against one of the most heavily mined targets in modern virology.

Subject of Research: Discovery of a natural product-like inhibitor of the SARS-CoV-2 main protease through virtual screening and in vitro validation

Article Title: Discovery of a new SARS-CoV-2 Mpro inhibitor from a natural product and natural product-like compound database using in silico and in vitro studies

Article References: Das, S., Pal, U., Begum, Y., Joshi, A., Singh, N., Thakur, K. G., & Singh, U. P. (2026). Discovery of a new SARS-CoV-2 Mpro inhibitor from a natural product and natural product-like compound database using in silico and in vitro studies. Molecular Diversity. https://doi.org/10.1007/s11030-026-11724-x

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11724-x

Keywords: SARS-CoV-2, Mpro, main protease inhibitor, virtual screening, natural products, molecular dynamics, MM-GBSA, FRET assay, differential scanning calorimetry, antiviral drug discovery, InterBioscreen, ADME prediction