1 Introduction

Coronaviruses are a type of virus family called Coronaviridae. They are crown viruses that can make people and animals sick, and they were identified after the first human infection in the 1960s. Coronaviruses cause most respiratory diseases in humans, and the infection symptoms range from mild colds to severe and sometimes fatal pneumopathies. In late 2019, the new Coronavirus (Sars-Cov-2) was discovered due to the emergence of new cases of pneumonia with various symptoms in the Chinese city of Wuhan. It got around fast, causing an epidemic in China and more patients in other countries worldwide [1]. COVID-19 refers to "Coronavirus Disease 2019," a highly contagious, emerging respiratory infectious viral zoonotic, potentially fatal in the elderly and those with chronic diseases. Traditional medicine, including knowledge, skills, and practices based rationally or not on a culture's theories, beliefs, and experiences, is used to keep people healthy and prevent, diagnose, treat, and cure physical and mental illnesses, according to WHO, 2003.

Medicinal plants include all plants whose organs contain one or more chemical substances that produce pharmacological activity. They represent the oldest and most widespread form of medication. Since the end of the nineteenth century, scientific research has made much progress using medicinal plants. They have proven to be an essential source for finding new molecules that can be used to create new medicines [2]. Plants have been, since antiquity, a natural source of treatment and pharmaceuticals for humans. Today a large part of the world's population, 80% according to the World Health Organization (WHO) in 2000, particularly in developing countries, uses herbal medicine [2, 3]. Over 500,000 known plant species have been treated and studied in the phytochemical and pharmacological fields. Each species may contain up to several hundred different constituents. Algeria has many traditional medicines and herbs used alone or together to treat other diseases. Indeed, it has 3000 species belonging to several plant families, of which 15% are endemic [2,3,4]. Due to their ability to treat many diseases and their relevant pharmacological properties, Eucalyptus, White Sagebrush, Noble Laurel, Cypress, and Nigella are known to be some of the most valuable and effective wild medicinal plants. They are the best choice for a phytochemical study that allows the evaluation of the relationships between plant and cultural diversity and the use of molecular docking studies, which are an essential part of phytochemical research [2,3,4]. The current research aims to investigate the therapeutic effect of some Algerian medicinal plants, primarily Eucalyptus, White Sagebrush, Noble Laurel, Cypress, and Nigel, to treat and inhibit COViD-19 using computational methods. Molecular docking methods and molecular dynamics simulation were used to explore the interaction mode between the main active biochemical compounds of Algerian plants and Sars-CoV-2 virus proteases. Figure 1 and Table 1 show Algerian medicinal plants of the current research with antiseptic properties [1, 2]. In this research, we could not conduct an in vitro study of the antiviral activity because Sars-CoV2 virus has yet to be isolated. And we adopted the effectiveness of these compounds based on previous studies in the laboratory of the antibacterial and antifungal properties of the active compounds extracted from Algerian medicinal plants showing that these plants have antiseptic properties.

Fig. 1
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A Eucalyptus globulus, B Artemisia herba alba. C Cupressus sempervirens L. [3, 4]

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Table 1 Algerian medicinal plants with antiseptic properties [5,6,7]
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2 Material and Methods

2.1 Computer Software and Databases

Innovative software, computerized databases introduced in the in-silico domain, and a Lenovo® microcomputer with a graphics card and a Core i3 processor. Computerized databases: PubChem Database deals with the structures, Physicochemical properties, and chemoinformatics of drugs, chemicals, and biologically active substances. Protein DataBank PDB database, a computerized database for the structural identification of biological targets, proteins, enzymes, DNA, etc. Software: Chemsketch software version 12.0.1 and Chem3D software version 18.0. Software for constructing and studying molecular structures. Auto-dock Software version 1.5.6. is a suite of automated docking tools. It predicts how small molecules, such as substrates or drug candidates, will bind to a known 3D structure receptor. Molegro Virtual Docker MVD version 6.0.1. is an integrated environment for studying and predicting the interaction of ligands with macromolecules. The Discovery Studio, version 17.2.0. is a suite of software to simulate systems of small molecules and macromolecules [8]. The product includes features to view and edit data and tools to perform database estimation [9, 10].

2.2 Molecular Docking Simulation

2.2.1 Sars-CoV-2 Protease Target Preparation

Molecular docking was used to study the interaction of active compounds of Algerian plants with the Sars-Cov-2 protease target [11, 12]. The target proteins are major (6WTT and 6LU7) proteases of the Sars-CoV-2 virus. The Sars-Cov-2 protease target has been downloaded from the Protein Data Bank PDB online protein database. Two Sars-Cov-2 protease complexes have been cocrystallized with two different native ligands. Two main criteria for choosing proteins are considered a resolution, which must be less than 2 Å, and the clash score. So, the X-ray diffraction method identified Sars-CoV-2 protease with inhibitor GC-376 and Sars-CoV-2 protease with inhibitor N3. The protease was prepared by removing the native molecule from the cocrystallized form and the water molecules using the software Molegro Virtual Docker MVD version 6.0.1 (2013). The protease target was prepared using the package MGLtools 1.5.6 by adding polar hydrogen. Then these files were introduced to Auto-Dock 1.5.6 software (Fig. 2).

Fig. 2
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The crystalline structure of Sars CoV-2 MPro A 6LU7 https://www.rcsb.org/structure/6lu7. B 6WTT https://www.rcsb.org/structure/6wtt. Chemical structure of natives ligands: C ligand N3. D Ligand GC-376 [13, 14]

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2.2.2 Preparation of Ligands

The chemical structures of ligands were downloaded from the PubChem database in 3D SDF format; https://pubchem.ncbi.nlm.nih.gov/.The molecular structures of selected ligands are illustrated in Fig. 3.

Fig. 3
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Chemical structure of the principal compound of plants [15]

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2.2.3 Molecular Docking Process

Molecular docking was done by Auto-dock 1.5.6 from January 2021 to March 2021. A research space (Grid Box) in the x, y, and z directions has been built; it is a three-dimensional cube encompassing the protein's active site. The active site parameters (Grid Box) were determined by Auto-dock v.1.5.6 for each of the 6LU7 and 6WTT protein molecules. Its values are centre_x = − 9.378, centre_y = 20.384, and centre_z = 68.898 for the 6LU7 protease target. Active site values are centre_x = 2.40252, centre_y = 27.6638, and centre_z = − 11.3782 for protease target 6WTT. The results were viewed by Discovery Studio software version 17.0.2. A new folder that includes the ligand and protease (the target) in the form of pdbqt, a text file «conf.txt» in which we mentioned all the parameters of the grid box, the identification of the ligands and the receptor, with the Auto-dock software file were created. Then the docking process for all the ligand molecules and their protease target was performed according to the general protocol described for the auto-dock vina, which appears in the link http://vina.scripps.edu [16].

2.2.4 Molecular Dynamics (MD) Simulations

The target system was set up using the web-based CHARMM-GUI [17,18,19] interface with the CHARMM36 force field [20]. The NAMD 2.13 [21] package was used for all the simulations. The periodic boundary conditions had dimensions of 97, 97, and 97 in x, y, and z, respectively, and were constructed using the TIP3P explicit solvation model [22]. The CHARMM general force field [23] was used for breeding the parameters for the best docking outcomes. Four Na+ ions were then used to neutralize the system. Production, equilibration, and minimization were all part of the MD procedures. All MD simulations used a 2-fs time step of integration. The production used the isothermal-isobaric (NPT) ensemble, whereas the equilibration was done with the canonical (NVT) ensemble. Utilizing the Nose'-Hoover Langevin piston barostat [24, 25] with a Langevin piston decay of 0.05 ps and a period of 0.1 ps, the pressure was maintained at 1 atm during the 100 ns of MD generation. The Langevin thermostat [26] set the temperature at 298.15 K. Lennard Jones interactions were smoothly trimmed at 8.0. A distance cutoff of 12.0 was applied to short-range nonbonded interactions with a pair list distance of 16. The particle-mesh Ewald (PME) approach [27, 28] handled long-range electrostatic interactions, with a grid spacing of 1.0 applied to every simulation cell. Using the SHAKE algorithm [29], all covalent bonds containing hydrogen atoms were limited. The same methodology is used for all MD simulations for consistency purposes.

2.2.5 Binding Energy Calculations

One-average molecular mechanics generalized Born surface area (MM/GBSA) approach executed by the MOLAICAL code [30,31,32]. The relative binding energy calculations, in which ligand (L) binds to the protein receptor (R) to form the complex (RL), which contributions of different interactions can represent:

$$\Delta G_{bind} = \Delta G_{RL} - \Delta G_{R} - \Delta G_{L}$$
$$\Delta G_{bind} = \Delta H - T\Delta S = \Delta E_{MM} + \Delta G_{Sol} - T\Delta S$$

The changes in the gas phase moleculenergy openar mechanics open meentropy opench isconformational entropy \(\left(-T\Delta S\right)\) are determined as follows: \(\Delta {E}_{MM}\) is the sum of the changes in the electrostatic energies \(\Delta {E}_{ele}\), the van der Waals enenergies capergies \(\Delta {E}_{vdW}\), and the internal energies \(\Delta {E}_{int}\) (bonded interactions); \(\Delta {G}_{Sol}\) is the total of both the polar solvation (calculated using the generalized Born model) and the nonpolar solvation (calculated using the solvent-accessible surface area) and \(-T\Delta S\) is calculated by the normal mode analysis. The solvent dielectric constant of 78.5 and the surface tension constant of 0.03012 kJ mol-1 Å2 were used for MM/GBSA calculations.

2.2.6 ADMET Analysis

The SwissADME, pkCSM, and ProTox II software were used for ADMET analysis [33, 34]. All ADME properties were determined by the SwissADME software, except the clearance calculated by the pkCSM software. All toxicities were evaluated by the ProTox-II software, except cardiac toxicity, which was analyzed by the hERG receptor inhibition using the pkCSM software. Nirmatrelvir was chosen as a reference, and this drug is used as an antiviral for the treatment of infection Sars-CoV-2, marketed as a pharmaceutical complex (Nirmatrelvir + Ritonavir) or Paxlovid®, the only formula having an early authorization for use in 2022, Nirmatrelvir also has a molecular weight of 499.5 g/mol, following the Lipinski rules (MW less than 500 g/mol).

3 Results and Discussion

3.1 Molecular Docking Results

3.1.1 Molecular Docking scores

The best score and average of redocking of native ligands with their biological target are presented in Table 2, Fig. 4.

Table 2 Best score and average of redocking of native ligands with their biological target
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Fig. 4
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A Interactions between the 6WTT protease target and the native inhibitor GC-376. B Interactions between the 6LU7 protease target and the N3 native inhibitor

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The best docking scores and averages for 6LU7 and 6WTT protease ligands are presented in Tables 3 and 4.

Table 3 Best docking scores and averages for 6LU7 protease ligands
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Table 4 Best docking scores and averages for 6WTT protease ligands
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3.1.2 Ligand Docking Poses

The poses were visualized for the common ligands between the studied plants and those of therapeutic interest mentioned above in the literature. The visualization was represented for the two targets, the Sars-CoV-2 proteases 6LU7 and 6WTT, on 2D and 3D (Figs. 5 and 6).

Fig. 5
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Poses of ligands with Sars CoV-2 M Pro 6LU7: A eucalyptol, B cedrol, C camphor, D thuyone

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Fig. 6
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Poses 2D and 3D ligands with Sars CoV-2 MPro 6WTT: A eucalyptol, B cedrol, C camphor, D thuyone, E globulol

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3.2 Molecular Dynamic results

3.2.1 Root Mean Square Deviations RMSD

RMSD was calculated for the complex based on 'Backbone' atoms using the VMD program. The mean RMSD value for Camphor, Cedrol, and Eucalyptol complexes are 1.652 ± 0.2175 Å, 2.96 ± 0.453 Å, and 1.894 ± 0.223 Å, respectively. According to the RMSD graph for the Sars-CoV-2 proteases 6LU7 and 6WTT backbone, the structure remained stable throughout the simulation process with slight variation within the range of 1, which is typical of globular proteins. RMSD was calculated for the ligand based on ligands atoms using VMD program. The mean RMSD values for Camphor, Cedrol, and Eucalyptol complexes are 0.841 ± 0.1809 Å, 0.8433 ± 0.1478, and 0.814 ± 0.1735. RMSD of ligands remained reasonably stable throughout the simulation for all three complexes (Fig. 7), Column A.

Fig. 7
figure 7

From left to right: A RMSD, B RMSF, and C Radius of gyration of the complexes during 100 ns MD simulation. Camphor (top), cedrol (middle), and eucalyptol (bottom)

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3.2.2 Root Mean Square Fluctuations RMSF

RMSF was calculated for the Sars-CoV-2 proteases 6LU7 and 6WTT—Camphor, Cedrol, and Eucalyptol complex based on ‘C-alpha’ atoms using the VMD program. The fluctuation intensity remains below 2.5 Å except for residues 1, 2, and 302 to 306, representing a loop or turns in the Protei (Fig. 5), Column B.

3.2.3 The Radius of Gyration (ROG)

The radius of gyration was calculated for the complex based on ‘C-alpha’ atoms using the VMD program. The mean ROG value for Camphor, Cedrol, and Eucalyptol- Sars-CoV-2 proteases 6LU7 and 6WTT complexes are 22.46 ± 0.105 Å, 22.33 ± 0.120 Å, and 22.46 ± 0.1145 Å. The slight fluctuation within the 1 Å Rog value during the MD simulation time indicates a slight opening and closing of the N and C terminal domains (Fig. 7), Column C.

3.2.4 Hydrogen Bonds (Protein–Ligand)

The total number of hydrogen bonds formed between Camphor, Cedrol, and Eucalyptol (ligand) and Sars-CoV-2 proteases 6LU7 and 6WTT (protein) during 100 ns of the simulation time are shown in Fig. 8, Column A. Camphor, Cedrol, and Eucalyptol (ligand) exhibits a consistent fluctuating number of H-bonds with Sars-CoV-2 proteases 6LU7 and 6WTT (protein) and remains in bound form. The fluctuations indicate that the ligand is changing conformation within the pocket (Fig. 8); column A. Table 5 shows the hydrogen bond occupancy values for each ligand–protein combination. The percentage of Sars-CoV-2 proteases 6LU7 and 6WTT (protein)-Camphor, Cedrol, and Eucalyptol (ligand) complex conformations out of 1000 conformations in which a detailed residue participates in a hydrogen bond is known as hydrogen bond occupancy. The 100 ns molecular dynamics trajectory was used to determine the 1000 conformations for each compound.

Fig. 8
figure 8

From left to right: A hydrogen bonds (protein–ligand) and B average distance between ligand and the protein for of the complexes during 100 ns MD simulation. Camphor (top), cedrol (middle), and eucalyptol (bottom)

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Table 5 Hydrogen-bonding occupancy details for each protein–ligand complex
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3.2.5 Average Center-of-Mass Distance

The average Center-of-Mass Distance between Camphor, Cedrol, Eucalyptol (ligand) and Sars-CoV-2 proteases 6LU7 and 6WTT (protein) during 100 ns of the simulation time is shown in Fig. 7, Column B. The mean distance for Camphor, Cedrol, and Eucalyptol complexes is 17.32 ± 0.0183 Å, 18.961 ± 0.018 Å, and 16.81 ± 0.0144 Å. All the ligands show a stable COM distance, with less than 1 Å of fluctuation (Fig. 7), Column B.

3.2.6 Contact Frequency (CF) Analysis

To further evaluate the binding between Sars-CoV-2 proteases 6LU7 and 6WTT (protein) and the tested (ligands) Camphor, Cedrol, and Eucalyptol, a contact frequency (CF) analysis was performed using the contactFreq.tcl module in VMD with a cutoff of 4 Å. The residues with higher CF% are shown in Table 6 and Fig. 8.

Table 6 Contact frequency analysis
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3.2.7 Principal Component Analysis (PCA)

Principal component analysis of the complex calculated from the Bio3D program of R (Fig. 9), Column A.

Fig. 9
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Contact frequency (CF) analysis

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3.2.8 Dynamic Cross-Correlation Matrix Analysis (DCCM)

Protein Residue dynamic cross-correlated motions for the complex were calculated from the Bio 3D program of R. The variation of color from red to white to blue reflects the intensity of correlated motion. Blue indicates a negative correlation; white indicates no correlation, and red signifies positively correlated motions between residues, as shown in Fig. 10, column B.

Fig. 10
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From left to right: A principal component analysis and B dynamic cross-correlation matrix analysis (DCCM). Camphor (top), cedrol (middle), and eucalyptol (bottom)

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3.2.9 Potential Energy, Pressure, and Temperature

The system potential energy, pressure, and temperature during 100 ns of MD simulation, as obtained from the NAMD log file, are shown in Fig. 9. The graph shows converged potential energy, pressure, and temperature throughout the 100 ns simulations (Fig. 11).

Fig. 11
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From left to right: temperature, pressure, and potential energy during the 100 ns MD simulations

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3.2.10 MMGBSA Binding Energy

As opposed to other computational free energy techniques like the free energy perturbation (FEP) or thermodynamic integration (TI) methods, the Molecular Mechanics/Generalized Born Surface Area (MM/GBSA) approach is the quickest force field-based method that computes the free energy of binding. Additionally, comparison tests have shown that Molecular Mechanics/Poisson Boltzmann Surface Area MM/GBSA performs better than MM/PBSA [35, 36].

The MolAICal software version 16 was used for the MM/GBSA computation. The MM/GBSA may be calculated using MolAICal's output data from NAMD 5 molecular dynamics simulations. Table 7 displays the computed binding free energy. The outcome demonstrates that the ligand remained in bound form for the simulation.

Table 7 Calculated binding free energies of tested compound [kcal/mol]
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3.2.11 ADME-T Results

The results of the ADME-Tox profile are listed in Tables 8 and 9. A satisfactory bioavailability score has been noted, as illustrated by a red plot belonging to the red area (typical values) in the radar chart. According to the SwissADME-created radar bioavailability chart, all active compounds have good bioavailability, with a score of 0.55 and good plots located in the radar chart red area, where the same score and radar chart were obtained for the Nirmatrelvir reference. The bioavailability calculation is based on 6 parameters: MW, lipophilic, polarity, insolubility, unsaturation, and flexibility (Fig. 12).

Table 8 ADME-Tox properties of best Hits and Nirmatrelvir, by Swiss ADME-Tox and pkCSM
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Table 9 Principal toxicities of best Hits and Nirmatrelvir, by ProToxII and pkCSM
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Fig. 12
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Radar chart for oral bioavailability of best Hits by SwissADME: A eucalyptol, B camphor, C cedrol, D nirmatrelvir

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All active compounds of Algerian plants are druglike, which means that these products have medicinal properties or can be biologically active according to the 5 Lipinski rules (MW, log P, number of acceptor hydrogen bonds, number of donor hydrogen bonds, and rotatable bonds).

All active compounds are not toxic and do not have significant toxicities: hepatotoxicity, cytotoxicity, carcinogenicity, immunotoxicity, cardiac toxicity, and AMES toxicity. Also, the same result was obtained for the Nirmatrelvir reference, shown by the red zone (average for the active molecule class) in the radar toxicity map.

4 Discussion

Based on chemical affinity results and literature data, the 6LU7 protein was selected as a reference target and the N3 native ligand as a reference for our work. All simulated ligands showed higher chemical affinity scores with the two targets, the Sars-CoV-2 virus proteases and the 6LU7 and 6WTT proteins, with lower interaction energy (binding affinity). Molecular docking with the two available targets proteases of Sars-CoV-2 on PDB; provided similar results for all ligands presenting the primary active ingredients of Algerian plants with anti-Covid-19 potential. Cedrol, camphor, and eucalyptol were given the highest scores for both simulated targets. Redocking native ligands with their specific biological target showed similar results for the two native ligands. The current results fit well with the results of two other published studies compared to the results of the present work. Namely, study 1 was carried out at Tlemcen in Algeria on "inhibition of the receptor of the angiotensin conversion enzyme 2 of Covid-19 by the components of Ammoides verticillata collected in western Algeria," which showed a range of − 4.2320 to − 5.7853 kcal/mol and an average of − 4.8237 kcal/mol. A second study on "Moroccan medicinal plants as COVID-19 inhibitors". Made in Meknes, Morocco, it showed an identical range from − 4.6 to − 6.1 kcal/mol and an average of − 5.25 kcal/mol [37].

By superimposing the results of our study and previously published data, we have identified the active site of Sars-CoV2 main protease, 6LU7. Principal amino acids that interact more with simulated ligands are histidine 41, histidine 163, histidine 172, cysteine 145, glycine 143, and glutamic acid 166. On the other hand, we have calculated 3D parameters X:9,378, Y: 20,384, Z: 68,898, corresponding to the active site size of target 6LU7, as established in Fig. 13 [38, 39].

Fig. 13
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The active site of Sars-Cov-2 main protease 6LU7 [15]

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The molecular dynamic results and the MMGBSA relative binding energies for the top docking compounds showed that 6lu7_cedrol was stable with = −11.62 ± 0.14 kcal/mol, 6lu7_camphor was stable but not on the original binding site with = −9.6 ± 0.17 kcal/mol, and 6lu7_Eucalyptol was unstable with = −4.16 ± 0.17 kcal/mol.

Most active compounds (Eucalyptol, Cedrol, and Camphor) have a CYP inhibition (CYP 2C19, CYP 2C9). The same result was noted for the reference drug Nirmatrelvir (CYP 3A4 inhibitor), which means that these molecules can interact with the associated medicines; they inhibit their metabolism, which can increase the concentration of associated products in the body if swallowed.

Toxic doses are often given as LD50 in mg/kg body weight. LD50 is the median lethal dose at which 50% of test subjects die after exposure to a compound. This study estimated simulated compounds with doses ranging from 500 mg/kg (thuyone) to 5000 mg/kg (camphene). Thus, the best score was noted for camphene, along with eucalyptol (22,480 mg/kg), globulol, and cedrol (2000 mg/kg); these compounds have an LD50 close to that of Nirmatrelvir (3000 mg/kg). Concerning these results, eucalyptol and camphene belong to the toxicity class V, which means that they can be harmful if swallowed, as well as the drug Nirmatrelvir. In contrast, globulol, camphor, and cedrol belong to toxicity class IV, which means they are harmful if swallowed. Toxicity classes are defined according to the GHS Globally Harmonized System of Classification for Chemical Labeling: Class IV: harmful (300 < LD50 2000), Class V: may be harmful (2000 < LD50 < 5000).

5 Conclusion

This manuscript consists of an Insilico study of the interaction of natural ligands with active ingredients of Algerian medicinal plants on the two targets of the identified proteases of Sars-CoV-2. Molecular docking has shown that all of the active compounds of Algerian medicinal plants simulated with Sars-CoV2 protease 6LU7 have the same chemical structure. Cedrol, Camphor, and Eucalyptol showed optimal interaction with higher scores. Active compounds of Algerian medicinal plants have also demonstrated an excellent pharmacokinetic and toxicological profile in ADME-Tox. However, molecular dynamics simulation showed the stability of Camphor-6LU7 and Cedrol-6LU7 complexes, favoring the biological potential of white artemisia and cypress plants. These results suggest that Cypress (Cupressus sempervirens L.) and White Artemisia (Artemisia herba alba L) are good candidates for an Invitro study. Considering the toxicity classes obtained 4 and 5, medicinal plants should be used with caution, with low doses in the case of oral herbal tea or pharmaceutical formulation, and by dilution in case of external use of essential oil extracted from plants [40,41,42,43,44,45,46,47,48,49].

On the other hand, the data in the literature support the antiseptic power of Algerian medicinal plants and confirm the positive results found in silico about the chemical affinity of the active compounds of the medicinal plant with the biological target of Sars-CoV2 by molecular docking and the pharmacological properties by ADME-Tox parameters [50,51,52,53].