Virtual Screening Uncovers Promising Non-Covalent Inhibitors of Human Rhinovirus 3C Protease

Human rhinoviruses, the dominant cause of the common cold, have long been dismissed as minor nuisances, yet their clinical footprint extends far beyond a runny nose. These viruses are strongly linked to asthma exacerbations, bronchiolitis in infants, and serious lower respiratory tract infections in children and vulnerable adults. Despite decades of effort, no broadly effective antiviral drug has reached the clinic for rhinovirus disease, and vaccine development has been hampered by the sheer number of circulating serotypes. A new computational study published in Molecular Diversity now reports the identification of two promising small-molecule candidates that could change that picture, using an extensive structure-based pipeline to target one of the virus’s most vulnerable enzymes.

The research, led by Hafs Essaadi and colleagues at Mohammed V University in Rabat, Morocco, together with collaborators at the Mohammed VI Center for Research and Innovation and UM6SS, focused on the viral 3C protease, an enzyme designated 3Cpro that is indispensable to the rhinovirus life cycle. After the virus infects a cell, its genome is translated into a single long polyprotein that must be cleaved into functional viral proteins, and the 3C protease performs most of these cuts. Because the catalytic architecture of 3Cpro is highly conserved across human rhinovirus species, a molecule that disables it could in principle suppress a wide range of rhinovirus strains, making the enzyme an attractive therapeutic target.

To find candidate inhibitors, the team screened a library of 49,437 compounds against the 3C protease of human rhinovirus species C, the rhinovirus group most frequently associated with severe asthma exacerbations. The docking calculations were carried out with AutoDock Vina, a widely used molecular docking engine that predicts how small molecules orient themselves within a protein binding pocket and estimates the binding affinity of each pose. The researchers then applied ADMET-based prioritization, filtering the top-scoring hits for acceptable absorption, distribution, metabolism, excretion, and toxicity profiles, a step designed to weed out compounds that might bind well in silico but fail as drug candidates.

The docking analysis converged on two lead compounds that occupied the catalytic pocket of the protease in orientations predicted to be highly favorable. Both molecules formed stabilizing interactions with key residues of the active site, including His40, Glu71, and Cys147, the latter being the catalytic cysteine that sits at the heart of the enzyme’s cleavage chemistry. When compared with rupintrivir, the best-known experimental rhinovirus 3C protease inhibitor, which functions as an irreversible covalent inhibitor, the two new compounds achieved comparable or better engagement of the catalytic site without forming covalent bonds, a property that could translate into improved safety profiles.

Docking scores alone are a crude measure of binding, so the team subjected the protein-ligand complexes to molecular dynamics simulations lasting 200 nanoseconds each, allowing the atoms to move under realistic physical forces and revealing whether the predicted binding poses remain stable over time. Both compounds stabilized the protease relative to the unbound, or apo, form of the enzyme. Compound 1 produced the lowest protein root-mean-square deviation, holding the overall structure of the protease at 1.21 plus or minus 0.22 angstroms from its starting conformation, while compound 2 yielded the lowest root-mean-square fluctuation for the critical Cys147 residue, at just 0.48 angstroms, indicating that the catalytic nucleophile itself was held unusually rigid in the presence of this ligand.

Ligand mobility within the binding pocket provided further evidence of durable binding. Over the course of the simulations, compound 1 displayed a ligand RMSD of 1.49 plus or minus 0.78 angstroms and compound 2 a value of 2.01 plus or minus 0.39 angstroms, whereas rupintrivir wandered considerably more, with a ligand RMSD of 4.83 plus or minus 0.89 angstroms. Lower ligand RMSD values indicate that a molecule stays anchored in its original binding pose rather than drifting or partially exiting the pocket, suggesting that the two new candidates maintain more persistent contact with the active site than the reference inhibitor under dynamic conditions.

To quantify binding strength more rigorously, the researchers applied the MM/PBSA method, which combines molecular mechanics energies with solvation models to estimate the free energy of binding from simulated trajectories. Both leads outperformed rupintrivir on this metric: compound 1 achieved an effective binding energy of minus 23.59 plus or minus 6.87 kilocalories per mole, and compound 2 reached minus 25.25 plus or minus 5.75 kilocalories per mole, compared with minus 19.43 plus or minus 4.46 kilocalories per mole for rupintrivir. The team complemented these calculations with free energy landscape analysis, a technique that maps the conformational states sampled during simulation and identifies the most thermodynamically stable configurations of each complex, providing an additional layer of confidence that the observed binding modes represent genuine energetic minima rather than transient artifacts.

The significance of a non-covalent mechanism deserves emphasis. Rupintrivir, which reached phase II clinical trials as a nasal spray, irreversibly modifies the catalytic cysteine, and while this reactivity underlies its potency, covalent inhibitors can raise concerns about off-target modification of human enzymes that rely on similar cysteine chemistry. Compounds that achieve strong binding through reversible, non-covalent interactions, as the two leads reported here appear to do, may offer a wider therapeutic window. The ADMET filtering applied during the study further suggests that the candidates were selected not only for potency but also for drug-like behavior, although the authors stress that computational predictions of this kind require experimental confirmation.

Indeed, the study stops short of laboratory validation, and the authors are explicit that compounds 1 and 2 should be regarded as promising candidates for further experimental testing rather than proven antivirals. Enzyme inhibition assays, antiviral activity measurements in infected cell cultures, and eventually pharmacokinetic and toxicity studies in vivo will be needed to determine whether the computational promise translates into real therapeutic value. The data generated during the study are available from the corresponding author upon request, and the work was supported in part by computational resources from the Pediatric Translational Clinical Research Unit.

Nevertheless, the study adds to a growing body of evidence that structure-based computational screening can accelerate antiviral discovery against rhinoviruses, a pathogen family that has historically frustrated drug developers because of its antigenic diversity. By anchoring the search on a conserved, essential enzyme and validating hits through a multi-layered pipeline of docking, long-timescale dynamics, free energy landscape mapping, MM/PBSA energetics, and ADMET profiling, the Moroccan team has delivered a shortlist of chemically tractable starting points. If subsequent experiments bear out the predicted potency of these molecules, the work could represent an early but meaningful step toward the first effective antiviral treatment for the infections that trigger many of the world’s asthma attacks and common colds.

Subject of Research: Computational discovery of non-covalent inhibitors targeting the human rhinovirus 3C protease

Article Title: Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis

Article References: Essaadi, H., Chourir, A., Kourou, J., Makhloufi, F., Hachlaf, O., Abidou, A., Boutayeb, S., Eljaoudi, R., Belyamani, L., Ibrahimi, A., Hakmi, M., & Hafidi, N. E. (2026). Computational discovery of novel human rhinovirus 3 C protease inhibitors: molecular docking, dynamic simulations, free energy landscape, MMPBSA and ADMET analysis. Molecular Diversity. https://doi.org/10.1007/s11030-026-11704-1

Image Credits: AI Generated

DOI: 10.1007/s11030-026-11704-1

Keywords: human rhinovirus, 3C protease, antiviral drug discovery, molecular docking, molecular dynamics simulations, MM/PBSA, free energy landscape, ADMET analysis, virtual screening, rupintrivir, common cold, asthma exacerbation