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

The research team, led by The-Huan Tran, Thai-Son Tran and Thanh-Dao Tran of the University of Medicine and Pharmacy at Ho Chi Minh City and Hue University in Vietnam, constructed an integrative in silico workflow to mine a molecular library derived from known inhibitors of both enzymes, including the cholinesterase drug rivastigmine and the monoacylglycerol lipase inhibitors JZL-184 and ABX-1431. Acetylcholinesterase breaks down the neurotransmitter acetylcholine, and its inhibition has long underpinned symptomatic Alzheimer’s therapy. Monoacylglycerol lipase, by contrast, degrades 2-arachidonoylglycerol, an endocannabinoid signaling lipid whose preservation has been linked to dampened neuroinflammation and enhanced glial immunity. Hitting both enzymes with a single molecule could, in principle, address cognitive decline and inflammatory damage at the same time.

Starting from 365 candidate compounds, the investigators carried out molecular docking against crystal structures of human acetylcholinesterase and human monoglyceride lipase, using AutoDock Vina to predict binding poses and scores. They then applied interaction-based filtering to retain only those ligands that reproduced the key contacts characteristic of known inhibitors, followed by in silico prediction of absorption, distribution, metabolism and excretion properties and toxicity. This successive narrowing of the chemical space reflects a widely adopted paradigm in early-stage drug discovery, where computational filters are stacked so that only chemically sensible, pharmacokinetically plausible candidates advance to the most expensive and time-consuming analyses.

One compound, designated H34, rose to the top of the ranking. Molecular dynamics simulations run with GROMACS under the CHARMM36 force field examined the structural stability of the H34-enzyme complexes over time, tracking the root-mean-square deviation of the protein backbone, the root-mean-square fluctuations of individual residues, the radius of gyration and the solvent-accessible surface area. Across these metrics, the H34 complexes displayed comparatively favorable stability, suggesting that the ligand does not disrupt the overall fold of either enzyme and remains seated in the binding pocket under physiologically realistic conditions.

To move beyond qualitative stability assessments, the team estimated binding free energies using the MM/GBSA end-state method implemented in the gmx_MMPBSA tool. H34 yielded estimated binding free energies of −30.96 and −37.34 kcal/mol for the two enzyme targets, values that compare favorably within the context of the screened series and support the compound’s predicted affinity for both proteins. These calculations decompose the interaction into electrostatic, van der Waals and solvation contributions, offering a thermodynamic rationale for why the molecule holds on to each active site.

Further dynamical profiling added depth to the picture. ProLIF interaction mapping, which encodes protein-ligand contacts as fingerprints across the simulation trajectory, showed that the interactions anchoring H34 in each active site persisted over time rather than flickering in and out. Free energy landscape analysis, built on dihedral angle principal component analysis, mapped the conformational behavior of the complexes and revealed energetically stable basins, indicating that the ligand-bound enzymes do not wander between widely divergent conformations. Together, these analyses portray a compound whose binding mode is not merely a static docking artifact but a durable, low-energy arrangement.

Drug-likeness and safety filters reinforced the computational case for H34. SwissADME-based pharmacokinetic predictions indicated favorable properties relevant to oral absorption and brain exposure, while ProTox 3.0 predictions pointed to low acute toxicity. Because any candidate intended for Alzheimer’s disease must reach the central nervous system without accumulating liability elsewhere, these early pharmacokinetic and toxicological signals, though still predictive rather than experimental, are a meaningful part of the triage process that decides which molecules justify synthesis and laboratory testing.

The study’s authors are careful to frame H34 as a candidate rather than a drug. All findings derive from computational models, and docking scores, MM/GBSA estimates and simulations can only approximate the thermodynamics and kinetics of real enzyme inhibition. The necessary next steps include chemical synthesis and in vitro enzyme assays to measure actual inhibitory potency against both acetylcholinesterase and monoacylglycerol lipase, followed by cell-based and ultimately animal studies to test whether the dual-target hypothesis translates into cognitive and anti-inflammatory benefit in living systems.

Even so, the work illustrates why integrated computational strategies have become indispensable in multi-target drug discovery. By combining ligand-based library generation, structure-based docking, interaction fingerprinting, molecular dynamics, free energy estimation and ADME and toxicity prediction in a single pipeline, the researchers compressed an enormous search space into one well-characterized hit in silico. If H34 or its analogs survive experimental validation, the compound family could contribute to a new generation of multi-target-directed ligands designed for the complexity of Alzheimer’s disease, where modulating a single pathway has repeatedly fallen short.

Subject of Research: Computational discovery of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer's disease

Article Title: Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer’s disease

Article References: Tran, T.-H., Tran, T.-S., & Tran, T.-D. (2026). Computational discovery and dynamic profiling of dual acetylcholinesterase and monoacylglycerol lipase inhibitors for Alzheimer’s disease. Molecular Diversity. https://doi.org/10.1007/s11030-026-11725-w

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11725-w

Keywords: Alzheimer's disease, acetylcholinesterase, monoacylglycerol lipase, dual inhibition, molecular docking, molecular dynamics, MM/GBSA, virtual screening, drug discovery, neuroinflammation, multi-target ligands, ADME prediction