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Background:SARS-CoV-2's remarkable capacity for genetic mutation enables it toswiftly adapt to environmental changes, influencing critical attributes, such as antigenicity andtransmissibility. Thus, multi-target inhibitors capable of effectively combating various viral mutants concurrently are of great interest. Objective:This study aimed to investigate natural compounds that could unitedly inhibit spikeglycoproteins of various Omicron mutants. Implementation of various in silico approaches allows us to scan a library of compounds against a variety of mutants in order to find the ones thatwould inhibit the viral entry disregard of occurred mutations. Methods:An extensive analysis of relevant literature was conducted to compile a libraryof chemical compounds sourced from citrus essential oils. Ten homology models representingmutants of the Omicron variant were generated, including the latest 23F clade (EG.5.1),and the compound library was screened against them. Subsequently, employing comprehensivemolecular docking and molecular dynamics simulations, we successfully identifiedpromising compounds that exhibited sufficient binding efficacy towards the receptorbinding domains (RBDs) of the mutant viral strains. The scoring of ligands was based ontheir average potency against all models generated herein, in addition to a reference OmicronRBD structure. Furthermore, the toxicity profile of the highest-scoring compounds waspredicted. Results:Out of ten built homology models, seven were successfully validated and showed to bereliable for In Silico studies. Three models of clades 22C, 22D, and 22E had major deviations intheir secondary structure and needed further refinement. Notably, through a 100 nanosecondmolecular dynamics simulation, terpinen-4-ol emerged as a potent inhibitor of the OmicronSARS-CoV-2 RBD from the 21K clade (BA.1); however, it did not show high stability in complexes with other mutants. This suggests the need for the utilization of a larger library of chemical compounds as potential inhibitors. Conclusion:The outcomes of this investigation hold significant potential for the utilization of ahomology modeling approach for the prediction of RBD’s secondary structure based on its sequence when the 3D structure of a mutated protein is not available. This opens the opportunitiesfor further advancing the drug discovery process, offering novel avenues for the development ofmultifunctional, non-toxic natural medications. 

The rapid spread of SARS-CoV-2 required immediate actions to control the transmission of the virus and minimize its impact on humanity. An extensive mutation rate of this viral genome contributes to the virus’ ability to quickly adapt to environmental changes, impacts transmissibility and antigenicity, and may facilitate immune escape. Therefore, it is of great interest for researchers working in vaccine development and drug design to consider the impact of mutations on virus-drug interactions. Here, we propose a multitarget drug discovery pipeline for identifying potential drug candidates which can efficiently inhibit the Receptor Binding Domain (RBD) of spike glycoproteins from different variants of SARS-CoV-2. Eight homology models of RBDs for selected variants were created and validated using reference crystal structures. We then investigated interactions between host receptor ACE2 and RBDs from nine variants of SARS-CoV-2. It led us to conclude that efficient multi-variant targeting drugs should be capable of blocking residues Q(R)493 and N487 in RBDs. Using methods of molecular docking, molecular mechanics, and molecular dynamics, we identified three lead compounds (hesperidin, narirutin, and neohesperidin) suitable for multitarget SARS-CoV-2 inhibition. These compounds are flavanone glycosides found in citrus fruits – an active ingredient of Traditional Chinese Medicines. The developed pipeline can be further used to (1) model mutants for which crystal structures are not yet available and (2) scan a more extensive library of compounds against other mutated viral proteins.