# [Structure-Guided Antiviral Design](/knowledge/bioinformatics/structure-guided-antiviral-design-computational-modeling-spike-protein-dynamics-emerging-coronaviruses): In Silico Docking and Molecular Dynamics of SARS-CoV-2 Spike Protein Variants ## Key Takeaways - Structure-guided computational approaches, including homology modeling, molecular docking, and molecular dynamics (MD) simulations, are critical for analyzing SARS-CoV-2 spike protein variants. These methods predict how mutations in the receptor-binding domain (RBD) alter binding affinity to ACE2 and impact antibody recognition, aiding in the prediction of zoonotic spillover risk and the rational design of antiviral agents. - Molecular docking accurately predicts the binding poses of ACE2 and neutralizing antibodies to the RBD, with docking scores correlating with experimentally determined binding affinities. For instance, Omicron's higher docking score to ACE2 aligns with its increased transmissibility, and docking can identify mutations that disrupt antibody binding, facilitating immune escape. - Molecular dynamics simulations provide a dynamic, time-resolved perspective on the RBD-ACE2 complex, revealing conformational fluctuations and binding stability. Variants may exhibit altered flexibility in the RBM loop, with some showing reduced fluctuations suggesting more stable binding interfaces, which is crucial for understanding infectivity. - Binding free energy calculations, such as MM-PBSA/GBSA, quantify the relative binding affinities of variant complexes and designed inhibitors. These calculations correlate well with experimental data and are essential for assessing the impact of mutations on ACE2 affinity and for evaluating the potency of novel antiviral candidates. - The insights gained from these in silico methods directly inform the design of antiviral agents, including peptide inhibitors mimicking the ACE2 interface, engineered nanobodies, and aptamers. MD simulations are used to assess the stability of these designed binders and confirm their intended binding modes. - Computational pipelines integrating docking and MD simulations with deep mutational scanning data enable quantitative mapping of immune escape mutations. This is vital for updating vaccine antigen design and for selecting monoclonal antibodies targeting conserved epitopes, and can also be applied to predict cross-species transmission risks in veterinary contexts. --- ## Introduction The spike glycoprotein of SARS-CoV-2 is the primary determinant of host cell entry and the principal target of neutralizing antibodies. Variants such as Delta and Omicron have accumulated mutations in the receptor-binding domain (RBD) that alter binding affinity to the angiotensin-converting enzyme 2 (ACE2) receptor and enable immune evasion [1, 2, 3]. Understanding the structural and dynamic consequences of these mutations at atomic resolution is essential for [predicting zoonotic spillover](/knowledge/bioinformatics/predicting-zoonotic-spillover-computational-modeling-bat-coronavirus-spike-protein-ace2-receptor-binding-dynamics) risk and for rational design of antiviral agents [4, 5, 6]. Structure-guided computational approaches, including [homology modeling](/knowledge/bioinformatics/homology-modeling-principles-and-practices), [molecular docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond), and [molecular dynamics simulations](/knowledge/bioinformatics/molecular-dynamics-simulations-of-proteins-and-force-fields), have become indispensable tools in this effort [7, 8, 9]. This article reviews the methodological pipeline for [structure-guided antiviral design](/knowledge/bioinformatics/structure-guided-antiviral-design-computational-modeling-spike-protein-dynamics-emerging-coronaviruses) as applied to SARS-CoV-2 spike protein variants. Emphasis is placed on in silico techniques that predict how sequence changes alter protein-protein interactions at the RBD-ACE2 interface and at epitopes recognized by neutralizing antibodies [1, 10]. The discussion draws on parallel studies of other SARS-CoV-2 targets, such as the main protease (Mpro) and papain-like protease (PLpro), to illustrate the general principles of computer-aided drug design [11, 12, 13, 14]. ## Structural Biology of the Spike RBD and ACE2 Interface The RBD of the SARS-CoV-2 spike protein adopts a five-stranded antiparallel beta-sheet core with a receptor-binding motif (RBM) that directly contacts the N-terminal helix of ACE2 [1, 6]. High-resolution crystal structures and cryo-electron microscopy maps have revealed that the RBD undergoes a hinge-like conformational transition between “up” and “down” states, with the up state being necessary for ACE2 engagement [4, 5]. Sequence surveillance data archived in the [GISAID](/knowledge/bioinformatics/the-global-initiative-on-sharing-all-influenza-data-gisaid) database demonstrate that mutations such as N501Y, K417N, and E484K in the RBM can increase ACE2 affinity or reduce antibody recognition [2, 10]. [Homology modeling](/knowledge/bioinformatics/homology-modeling-principles-and-practices) is often the first computational step when a variant structure is unavailable [15]. Using a known template structure (e.g., PDB 6M0J for the wild-type RBD-ACE2 complex), residues are mutated and the local geometry is optimized with energy minimization [16]. The accuracy of the model depends on sequence identity; for SARS-CoV-2 variants, identity exceeds 95%, making template-based modeling reliable [5, 6]. Model quality can be assessed with Ramachandran plots and MolProbity scores [16]. ## [Molecular Docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond) of Variant RBD to ACE2 and Antibodies [Molecular docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond) predicts the preferred orientation of a ligand (e.g., ACE2 or an antibody fragment) when bound to the RBD [1, 10]. Rigid and flexible docking protocols are used. In rigid docking, both receptor and ligand are treated as static; in flexible docking, selected side chains are allowed to rotate to accommodate induced fit [16]. Common algorithms include AutoDock Vina and Glide, which use scoring functions that evaluate van der Waals, electrostatic, and desolvation contributions [15, 16]. For the RBD-ACE2 complex, docking calculations can recapitulate the crystallographic binding mode with root-mean-square deviations below 2.0 Å [1, 3]. Docking scores correlate with experimentally measured binding affinities; for example, the Omicron RBD shows a higher docking score toward ACE2 compared to the wild type, consistent with its increased transmissibility [2]. Docking is also used to predict the impact of RBD mutations on antibody binding [10]. A panel of neutralizing antibodies can be docked to variant RBD models to identify mutations that disrupt key hydrogen bonds or salt bridges, thereby enabling immune escape [4, 6]. ## [Molecular Dynamics Simulations](/knowledge/bioinformatics/molecular-dynamics-simulations-of-proteins-and-force-fields) of the RBD-ACE2 Complex Molecular dynamics (MD) simulations provide a time-resolved view of conformational fluctuations and binding stability. Simulations are typically run for tens to hundreds of nanoseconds using all-atom force fields (e.g., AMBER ff14SB, CHARMM36) and explicit solvent models (e.g., TIP3P) [14, 16]. The RBD-ACE2 complex is solvated in a water box, neutralized with counterions, and energy minimized before production runs. Temperature and pressure are controlled using algorithms such as Langevin dynamics and the Parrinello-Rahman barostat [14, 15]. MD trajectories are analyzed to compute root-mean-square fluctuation (RMSF), radius of gyration (Rg), and intermolecular hydrogen bond occupancy [5, 16]. Variants often exhibit altered flexibility in the RBM loop; for instance, the Omicron RBD shows reduced RMSF in regions that contact ACE2, suggesting a more stable binding interface [2, 3]. Principal component analysis can reveal collective motions relevant to conformational selection [14]. ## Binding Free Energy Calculations End-point free energy methods such as Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) or Generalized Born Surface Area (MM-GBSA) are routinely applied to estimate the relative binding affinity of variant complexes [1, 14]. These methods average the gas-phase molecular mechanics energy, solvation free energy, and entropic contribution over an MD trajectory. The MM-PBSA approach has been validated against experimental binding constants for RBD-ACE2 [1, 2]. Studies comparing wild-type and variant RBDs have shown that MM-PBSA computed ΔΔG values correlate well with surface plasmon resonance measurements [1, 3]. For example, the N501Y mutation enhances binding by approximately 1.5 kcal/mol through improved pi-pi stacking with ACE2 residue Y41 [2]. Similarly, E484K reduces antibody binding by 2.3 kcal/mol due to loss of a salt bridge with the antibody complementarity-determining region [10]. ## Structure-Guided Design of Antiviral Agents The atomic-level insights obtained from docking and MD simulations directly inform the design of entry inhibitors, nanobodies, and peptide mimetics [1, 4, 5]. Structure-guided optimization has been a central theme in developing SARS-CoV-2 antivirals across multiple targets [7, 8, 9, 11, 17, 12, 18, 19, 20, 13, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]. For spike, three classes of designed agents are prominent: 1. Peptide inhibitors that mimic the ACE2 interface. Ferková et al. designed proteomimetics that compete with ACE2 for RBD binding, using the structure of the RBD-ACE2 interface as a template [1]. These compounds showed nanomolar affinity in vitro. 2. Nanobodies engineered against the RBD. Hannula et al. used computational docking to select nanobody variants with enhanced neutralization breadth [4]. Jiang et al. designed trivalent nanobody clusters that simultaneously bind three RBDs, achieving avidity-driven potency [6]. 3. Aptamers and other synthetic binders. Rahman et al. developed bivalent aptamers that block RBD-ACE2 interaction by bridging two RBD monomers [10]. MD simulations are used to assess the stability of designed binders and to confirm that they engage the intended epitope without inducing unfavorable conformational changes [3, 5]. The binding free energy of the designed binder to the RBD is computed with MM-PBSA and compared to that of the natural receptor [1, 2]. ## Predicting Immune Escape and Antigenic Drift Combining docking of antibody panels with MD-based binding free energy calculations enables quantitative mapping of escape mutations [10]. [Deep mutational scanning](/knowledge/bioinformatics/deep-mutational-scanning-machine-learning-sars-cov-2-rbd-escape-mutations) data can be integrated into these pipelines to prioritize mutations that simultaneously increase ACE2 affinity and reduce antibody recognition [2, 3]. This approach is instrumental for updating vaccine antigen design and for selecting monoclonal antibodies that target conserved epitopes [4, 6]. For veterinary applications, predicting how emerging variants might adapt to ACE2 orthologs of domestic animals (e.g., cats, dogs, ferrets, livestock) is crucial for risk assessment [1, 5]. Homology models of animal ACE2 can be built from the human template, and docking scores with various RBD variants can highlight potential spillover hosts [2, 3]. Such cross-species analyses rely on the same structure-guided framework described above. ## Workflow Overview The following Mermaid diagram summarizes the computational workflow for [structure-guided antiviral design](/knowledge/bioinformatics/structure-guided-antiviral-design-computational-modeling-spike-protein-dynamics-emerging-coronaviruses) targeting the SARS-CoV-2 spike protein: ```mermaid flowchart TD A["Retrieve variant sequence from GISAID"] --> B["Build homology model of RBD"] B --> C["Validate model (Ramachandran, MolProbity)"] C --> D["Prepare receptor (PDB, protonation, grid generation)"] C --> E["Prepare ligand (ACE2 ectodomain or antibody Fv)"] D --> F["Molecular docking (AutoDock Vina, Glide)"] E --> F F --> G["Select top poses based on docking score"] G --> H["MD equilibration (NVT, NPT) and production run"] H --> I["Trajectory analysis (RMSF, H-bonds, PCA)"] I --> J["MM-PBSA/GBSA binding free energy calculation"] J --> K{"Acceptable ΔG?"} K -->|"Yes"| L["Identify key pharmacophore features"] K -->|"No"| M["Refine model or mutate RBD"] L --> N["Design inhibitor (peptide, nanobody, aptamer)"] N --> O["Dock inhibitor to RBD"] O --> P["MD of complex and MM-PBSA"] P --> Q["Experimental validation"] ``` ## Tables of Key Software and Methods | Step | Software / Method | Application | |---|----------|-------| | [Homology modeling](/knowledge/bioinformatics/homology-modeling-principles-and-practices) | MODELLER, SWISS-MODEL | Generate 3D models of variant RBD [15, 16] | | [Molecular docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond) | AutoDock Vina, Glide | Predict binding pose and score of ACE2/antibody [1, 10] | | Molecular dynamics | AMBER, GROMACS, CHARMM | Simulate conformational dynamics of complexes [5, 14] | | Binding free energy | MM-PBSA, MM-GBSA | Estimate relative binding affinities [1, 2] | | Structure validation | MolProbity, PROCHECK | Assess model quality [16] | | Sequence surveillance | GISAID | Track emerging variants for modeling input [2, 3] | | Drug Target | Structure-Guided Approach | Representative Studies | |-------|--------------|------------| | Spike RBD-ACE2 | Proteomimetics, nanobodies, aptamers | [1, 4, 6, 10] | | Main protease (Mpro) | Covalent and noncovalent inhibitors | [7, 8, 9, 18, 20, 13, 14, 23, 24, 25, 26, 29, 15, 31, 33, 34] | | Papain-like protease (PLpro) | Covalent inhibitors | [11, 12] | | nsp14 methyltransferase | Adenosine mimetics, SAM analogs | [17, 18, 19, 20] | | nsp16 methyltransferase | Allosteric inhibitors | [21] | | nsp15 endoribonuclease | Repurposed drug docking | [16] | | RNA-dependent [RNA polymerase](/knowledge/bioinformatics/rna-polymerase-structure-transcription-mechanisms) | PNA antisense oligomers | [22, 23] | ## Conclusion [Structure-guided antiviral design](/knowledge/bioinformatics/structure-guided-antiviral-design-computational-modeling-spike-protein-dynamics-emerging-coronaviruses) that integrates [homology modeling](/knowledge/bioinformatics/homology-modeling-principles-and-practices), [molecular docking](/knowledge/bioinformatics/docking-algorithms-autodock-glide-and-beyond), and [molecular dynamics simulations](/knowledge/bioinformatics/molecular-dynamics-simulations-of-proteins-and-force-fields) provides a powerful framework for understanding SARS-CoV-2 spike protein variant behavior and for developing countermeasures. The same methods successfully applied to protease and methyltransferase targets [7, 8, 9, 11, 17, 12, 19, 13, 14, 23, 24, 25, 26, 28, 29, 30, 31, 32, 33, 34, 35] are now being adapted to the spike protein, where they inform both antibody escape prediction and the rational design of entry inhibitors [1, 2, 3, 4, 5, 6, 10]. Continued integration with high-throughput sequence surveillance and [deep mutational scanning](/knowledge/bioinformatics/deep-mutational-scanning-machine-learning-sars-cov-2-spike-antibody-escape) will further enhance the predictive power of these in silico pipelines. ## References [1] Ferková S, Fayolle A, Boisvert O et al. Structure-Guided Design of Proteomimetics Targeting the SARS-CoV-2 S-RBD/hACE2 Interface. J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42289940/ [2] Ortega Del Campo S, Fernández Ballester GJ, Blanes Mira C et al. Synergistic antiviral effects of structure-guided peptides and a mutagenic base analog on SARS-CoV-2 replication. Antimicrob Agents Chemother. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42037394/ [3] Zannella C, Fernandez FR, Santoro F et al. Structure-Guided Design of Temporin-Derived Peptides Reveals Potent Dual-Mechanism Inhibitors of SARS-CoV-2. J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41622708/ [4] Hannula L, Kuivanen S, Lasham J et al. Nanobody engineering for SARS-CoV-2 neutralization and detection. Microbiol Spectr. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38363137/ [5] Qin Q, Jiang X, Huo L et al. Computational design and engineering of self-assembling multivalent microproteins with therapeutic potential against SARS-CoV-2. J Nanobiotechnology. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38341574/ [6] Jiang X, Qin Q, Zhu H et al. Structure-guided design of a trivalent nanobody cluster targeting SARS-CoV-2 spike protein. Int J Biol Macromol. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38000614/ [7] Alwethaynani MS, Hassan RN, Hussain R et al. Structure-guided optimization of N-(5-((2-oxopropyl) thio)-1,3,4-thiadiazol-2-yl) propionamide frameworks as potent SARS-CoV-2 M(pro) inhibitor for treating coronavirus disease. Bioorg Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42314596/ [8] Nguyen HN, Ranasinghe PS, Ilesinghe IKRS et al. Structure-guided design of broad-spectrum inhibitors of coronaviral proteases embodying a 1,3,2-oxazaphospholidin-3-one scaffold as a versatile design element. Eur J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/42208369/ [9] van der Straat R, Oerlemans R, Cong Y et al. Ugi-Tetrazole-Derived α‑Aminomethyl Scaffolds Reveal Unexpected Binding Modes in SARS-CoV‑2 3CLpro. ACS Med Chem Lett. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41982734/ [10] Rahman MS, Han MJ, Kim SW et al. Structure-Guided Development of Bivalent Aptamers Blocking SARS-CoV-2 Infection. Molecules. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/37375202/ [11] Wang J, Xu Y, Yang Y et al. Structural Basis and Inhibitor Development of SARS-CoV-2 Papain-like Protease. Molecules. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41683451/ [12] Sharafi M, Teh WP, Green J et al. Structure-Guided Design of Potent and Selective Covalent Inhibitors Targeting the SARS-CoV-2 Papain-like Protease. J Med Chem. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41557701/ [13] Okabe A, Carney DW, Tawada M et al. Discovery of Highly Potent Noncovalent Inhibitors of SARS-CoV-2 Main Protease through Computer-Aided Drug Design. J Med Chem. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41076627/ [14] Bhagat A, Kelam LM, Samanta N et al. Structure-guided design and triplicate molecular dynamics evaluation of mutant peptide inhibitors targeting SARS-CoV-2 main protease (Mpro). In Silico Pharmacol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/40917523/ [15] El Bakri Y, Ahmad B, Saravanan K et al. Insight into crystal structures and identification of potential styrylthieno[2,3-b]pyridine-2-carboxamide derivatives against COVID-19 Mpro through structure-guided modeling and simulation approach. J Biomol Struct Dyn. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/37318002/ [16] Jha P, Saluja D, Chopra M. Structure-guided pharmacophore based virtual screening, docking, and molecular dynamics to discover repurposed drugs as novel inhibitors against endoribonuclease Nsp15 of SARS-CoV-2. J Biomol Struct Dyn. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/35652904/ [17] Rosas-Lemus M, Athe S, Minasov G et al. S‑Adenosylhomocysteine Analogs Selectively Suppress Pan-Coronavirus Replication by Inhibition of nsp14 Methyltransferase. ACS Med Chem Lett. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41568342/ [18] Hausdorff M, Delpal A, Barelier S et al. Structure-guided optimization of adenosine mimetics as selective and potent inhibitors of coronavirus nsp14 N7-methyltransferases. Eur J Med Chem. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/37192550/ [19] Bobileva O, Bobrovs R, Sirma EE et al. 3-(Adenosylthio)benzoic Acid Derivatives as SARS-CoV-2 Nsp14 Methyltransferase Inhibitors. Molecules. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/36677825/ [20] Nencka R, Silhan J, Klima M et al. Coronaviral RNA-methyltransferases: function, structure and inhibition. Nucleic Acids Res. 2022. URL: https://pubmed.ncbi.nlm.nih.gov/35018474/ [21] Inniss NL, Kozic J, Li F et al. Discovery of a Druggable, Cryptic Pocket in SARS-CoV-2 nsp16 Using Allosteric Inhibitors. ACS Infect Dis. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/37728236/ [22] Shehzadi K, Kalsoom I, Yu MJ et al. Design and in-silico evaluation of PNA-based novel pronucleotide analogues targeting RNA-dependent [RNA polymerase](/knowledge/bioinformatics/rna-polymerase-structure-transcription-mechanisms) to combat COVID-19. J Biomol Struct Dyn. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/39937582/ [23] Shehzadi K, Yu M, Liang J. De Novo Potent Peptide Nucleic Acid Antisense Oligomer Inhibitors Targeting SARS-CoV-2 RNA-Dependent [RNA Polymerase](/knowledge/bioinformatics/rna-polymerase-structure-transcription-mechanisms) via Structure-Guided Drug Design. Int J Mol Sci. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/38139312/ [24] Mik V, Benz LS, Voller J et al. A patent review of Mpro protease inhibitors for the treatment of COVID-19 infections (2020 - present). Expert Opin Ther Pat. 2026. URL: https://pubmed.ncbi.nlm.nih.gov/41348159/ [25] Krishnathas R, Mineev KS, Fourkiotis NK et al. Structure-Based Rational Design of a Selective Hydrolase Inhibitor of the Severe Acute Respiratory Syndrome Coronavirus-2 Nsp3 Macrodomain. Chembiochem. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41176653/ [26] Stewart J, Jia R, Ali MA et al. Structure-Guided Temporin L Analogs Development to Inhibit the Main Protease of SARS-CoV‑2. ACS Med Chem Lett. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/41089481/ [27] Kuhn AJ, Outlaw VK, Marcink TC et al. Enhancing the solubility of SARS-CoV-2 inhibitors to increase future prospects for clinical development. J Virol. 2025. URL: https://pubmed.ncbi.nlm.nih.gov/39902960/ [28] Yin DH, Xin J, Chen S et al. Structure-guided design and photochemical synthesis of new carbamo(dithioperoxo)thioates with improved potencies to SARS-CoV-2 3CL(pro). Bioorg Med Chem. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/39442491/ [29] Dampalla CS, Kim Y, Zabiegala A et al. Structure-Guided Design of Potent Coronavirus Inhibitors with a 2-Pyrrolidone Scaffold: Biochemical, Crystallographic, and Virological Studies. J Med Chem. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38953866/ [30] Papini C, Ullah I, Ranjan AP et al. Proof-of-concept studies with a computationally designed M(pro) inhibitor as a synergistic combination regimen alternative to Paxlovid. Proc Natl Acad Sci U S A. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38621119/ [31] Westberg M, Su Y, Zou X et al. An orally bioavailable SARS-CoV-2 main protease inhibitor exhibits improved affinity and reduced sensitivity to mutations. Sci Transl Med. 2024. URL: https://pubmed.ncbi.nlm.nih.gov/38478629/ [32] Kang KM, Jang Y, Lee SS et al. Discovery of antiviral SARS-CoV-2 main protease inhibitors by structure-guided hit-to-lead optimization of carmofur. Eur J Med Chem. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/37633203/ [33] Dampalla CS, Miller MJ, Kim Y et al. Structure-guided design of direct-acting antivirals that exploit the gem-dimethyl effect and potently inhibit 3CL proteases of severe acute respiratory syndrome Coronavirus-2 (SARS-CoV-2) and middle east respiratory syndrome coronavirus (MERS-CoV). Eur J Med Chem. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/37080108/ [34] Dampalla CS, Nguyen HN, Rathnayake AD et al. Broad-Spectrum Cyclopropane-Based Inhibitors of Coronavirus 3C-like Proteases: Biochemical, Structural, and Virological Studies. ACS Pharmacol Transl Sci. 2023. URL: https://pubmed.ncbi.nlm.nih.gov/36654747/ [35] Dampalla CS, Rathnayake AD, Galasiti Kankanamalage AC et al. Structure-Guided Design of Potent Spirocyclic Inhibitors of Severe Acute Respiratory Syndrome Coronavirus-2 3C-like Protease. J Med Chem. 2022. 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