Research Article
A Molecular Modelling Approach of Some Keto-Based Natural Drugs with Potent Antiviral Activity Towards SARS-Cov-2
Sonali Priyadarshini Parida, Ankita Bhuyan, Pragyan Parimita Dash, Daffodil Mohanta, Amrutendu Pati, Shubhashree Swain, Mahediali Palsaniya, Mriganka Das* and Bidyut Kumar Kundu
Corresponding Author: Mriganka Das, School of Science, Gujarat State Fertilizers and Chemicals Limited (GSFC) University, Vadodara-391750, Gujarat, India.
Received: March 16, 2021; Revised: May 05, 2021; Accepted: June 27, 2021 Available Online: July 08, 2021
Citation: Parida SP, Bhuyan A, Dash PP, Mohanta D, Pati A, et al. (2022) A Molecular Modelling Approach of Some Keto-Based Natural Drugs with Potent Antiviral Activity Towards SARS-Cov-2. J Clin Trials Res, 5(3): 257-266.
Copyrights: ©2022 Parida SP, Bhuyan A, Dash PP, Mohanta D, Pati A, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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COVID-19, produced by SARS-CoV-2 has just inaugurated as a global pandemic. Intense efforts are also underway to discover a drug or vaccine across the globe to resist the outbreak. Nowadays, alternative treatments for the management of the disease are also being explored. It has been proven that phytochemicals from various plant extract are the promising candidate in dealing many therapeutic activities with minimum side effects. Herein, this article will cover the major uses of keto based natural drugs such as pulegone, camphor, thymoquinone for their antimicrobial properties against the proteases of receptor binding domain, which is one of the vital targets for novel antiviral agents. In order to discover the new possible COVID-19 inhibitors, the proteases PDBs e.g., 2gtb and 6lu7 will be used as hosts to calculate the interactions with those aforementioned natural drugs as guests. Those protein compounds are chosen because they share 96% similarity of antiviral nature. A comprehensive molecular docking approach (through both Auto dock and CCDC GOLD) have been introduced for the theoretical calculations of binding energies as a result of host-guest interactions. Furthermore, protein ligand interaction profiler (PLIP) server also provides hydrophobic interactions (clear data about amino acid residues along with bond distances), 3D view of molecules, number of hydrogen bonds, etc. Present research shows that pulegone is the most active having binding energy -5.93 kcal/ mol against 2gtb viral spike-protein, whereas, on the other, that of camphor is -4.92 kcal/ mol against 6lu7 protease of SARS-CoV-2.

INTRODUCTION

In the late 2019, an infection in Wuhan city of Hubei, China was identified from a tainted human, which was subsequently known as severe acute respiratory syndrome (SARS) Corona virus2i.e., SARS-CoV-2 [1,2]. This infection is a contaminated illness related with human-to-human transmission influencing the respiratory framework as well as influence the stomach related problems fundamentally [3]. Till February 2021, there are 112,456,453 affirmed cases and 2,497,514 passing around the world. America and Europe are worstly influenced alongside India, with millions of cases. Pacific, Asia and African nations are additionally lethally influenced [4].

Scientists are continuously working for the improvement of immunization and medications to treat COVID-19. Also, Food and Drug Administration (FDA) redeployed affirmed drugs for the adequacy against SARS-CoV-2. One next to the other plant-based therapies are additionally explored to fix or dealt with the illnesses. Despite the fact that in west nations, manufacturing mixtures are being utilized in the last decade, where regular Phyto-compounds are likewise under research. Moreover, bioactive Phyto-intensifies which were generally utilized, have now become vital medications to treat viral illnesses. There is an assorted combination of natural mixtures overwhelmingly made out of terpenes, terpenoids, phenylpropanoids, and aldehydes [5-7]. Distinctive normally happening natural keto-intensifies shows antimicrobial, antibacterial, and antiviral properties.

Hence, natural mixtures based remedial methodology can be focused, which are composed of organic compounds having hydroxyl and keto groups. Past investigations show that numerous fundamental mixtures, where the major counterpart is hydroxyl functional group based organic ligands, have been successful in hindering attack of microorganisms. With regards to this investigation identified with antiviral properties, a couple of fundamentals organic ligands have been viable against RNA and DNA infections, for example, avian fluan infection [8], herpes simplex infection type 1 (HSV-1) and type 2 (HSV-2), dengue infection type 2, Junín infection, flu infection adenovirus type 3, poliovirus, and coxsackievirus B1 [9].

Since there is a limited number of a medication accessible for treating viral illnesses, specific understanding towards drug developments thus unmet need of current research. To evaluate the restorative capability of specific drug, it is fundamental for knowing the association between the ligands and the protein at sub-atomic level. An underlying in- silico study including numerous methods impressively can decreases the time required for this drug discovery processes [10-12]. Through computational investigation, it is too easy to find out viable outcomes about protein-ligand or host-guest interactions by analyzing some specific parameters, which can help further in targeted drug designing.

In persistence our recent advancements on biological effort on antimicrobial [13-16], anticancer [13,17-20], bioinspired catalysis [18,21-24] and host-guest bio-conjugative studies [17,18,25-28], our emphasis of this article will be the discovery of some outstanding antiviral drugs though in silico techniques. Thus, in this report, we have chosenSARS-CoV-2 principle protease as host for situation with some keto-based naturally available organic ligands [29,30]. The protease receptor proteins via 2gtb and 6lu7 have been selected as these are answerable for the combination of viral effect with 96% similarity and cellular imbalance in our body [31,32]. Moreover, keto-based ligands are yet not explored much via theoretical simulation in comparison to hydroxy analogous. Therefore, in the current examination, molecular docking and the applied density functional theory (DFT) [13,15,18,24,26-28] approach have been used to evaluate the antiviral properties of significant segments of certain fundamental natural keto-based drugs extracted from various plants that are outstanding for antimicrobial action. Using molecular docking, hydrogen bonding and hydrophobic connections have been checked properly in the stable collaboration between the protein and ligand (Scheme 1).

MATERIALS AND METHODS

Selection and preparation of host protein structure

The protease subunit of SARS-CoV-2 proteins such as PDB ID: 6lu7 and PDB ID: 2gtb were particularly chosen as the target with selected ligand molecules. The reason behind the selection of protease subunit is because of its strong contribution towards viral effect. Those host proteins were downloaded from the RCSB protein data bank in pdb format. Pymol was then used for visualizing 3D structure followed by the removal of water molecules and unwanted species bounded to the host pdb. After successful generation of pdb host from Pymol, Autodock software was taken for adjusting various charges and energies associated with the pdb of hosts. Rather pdb was converted to pdbqt format using the same software for further uses [33,34].

Selection and preparation of ligands

We have taken three keto compounds as ligands viz thymoquinone, camphor and pulegone. These ligands along with their hydroxyl analogues are known for its antiviral, and/ or anti-bacterial activities. These are selected as guests against the SARS-CoV-2 protease-based hosts viz; 2gtb and 6lu7. Now, ChemDraw3D along with MM2 performance has been used to generate each structure of ligand for finding out the detail information about their exact chemical composition. The exported pdb of the ligands from ChemDraw further used to calculate the geometry optimized structure through density functional theory (DFT). These ligands were screened at Auto dock software which converts pdb files into pdbqt format. This process involves detecting torsion root, adjusting torsion angle assigning charges and changing them to pdbqt format for their further use during molecular docking [13,18,35].

Theoretical calculations of ligand structures

Density functional theory (DFT) gives us an idea of solid-state structure of a molecule using the quantum formulations [36]. Another field of DFT is to state about chemical behavior by using electron density calculation. In order to achieve the geometry optimized structures, Gaussian09 programme having B3LYP basis set and 6-31G(+)d,p functional have been used for each ligand. Furthermore, different parameters have been elucidated via DFT calculations, which includes total energy, molecular dipole moment, lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) energies, band gap (ΔE), absolute hardness (ɳ), fraction of electrons transfer (ΔN), and electro negativity (χ) [16].

Molecular docking

In-silico technique anticipate suitable interconnection between protein molecules of hosts and small guests (viz. ligand) based on their geometry and structure [37]. In this report, molecular docking is performed by using certain software and servers such as Autodock, CCDC Gold, PLIP, Open Bable GUI, Chimera, and Protein. Plus.

In Autodock, the accuracy and speed are maintained throughout the steps. The protein and the ligand molecules were selected with little adjustment of Kollman, and residual charges. Apart from that the structure is dehydrated and hetero atom(s) were deleted wherever applicable. Furthermore, torsion tree grid and spacing were set followed by running ‘auto grid’ and ‘auto dock’ programmes to get the host-guest interaction based docked results in the form of binding energy and inhibition constant [38].

The best dock pdb file obtained in Autodock were selected and converted to pdbqt format in open bable tool. The cavity site recognition is known to be preliminary step for protein binding site recognition was done through protein plus server and the 3D structure of molecules was observed through PLIP server.

Besides, CCDC Gold work proceeded by choosing HERMES tool, in which protein in pdb format is loaded followed by defining binding sites. Mol2 file of ligand prepared from CCDC Mercury, was selected by using ligand flexibility, GA setting, scoring function and all other required functions to run programme, which after completion gives docking solutions/ poses.

Chimera is an extensible program for instinctive portrayal and examination of nuclear developments and related data, including thickness maps, supra molecular social occasions, gathering game plans, docking results, bearings, and conformational outfits. Structural analysis is done using this, all the residues were also named using name specifier option. At last interaction poses and session were saved for further use.

RESULTS AND DISCUSSION

Molecular Docking

Docking technique is an in-silico technique, where the measurement of binding energy parameters and scores are used to predict how a protein interacts with ligands [39-41]. To identify potential antiviral activity, all the ligands taken are docked against host SARS-CoV-2 proteins. In this case docking result shows that pulegone gives the lowest value of binding energy (-5.93 kcal/ mol) during complexation with 2gtb and it is the best score as compared to other docked outcomes. The binding energy in case of thymoquinone (-5.78 kcal/ mol) as well as camphor (-5.70 kcal/ mol) is lower than that of pulegone. One hydrogen bonding interaction with amino acid GLN192 (H-donor) and three hydrophobic interactions via amino acid residues LEU167, PRO168, GLN192 are shown by pulegone in complexation with the host 2gtb. The hydrogen bonding and hydrophobic interaction between the ligand and the protein 2gtb are summarized in Table 1.

Docking results with PDB ID 6lu7 display that camphor gives best score (-4.92 kcal/ mol) than that of pulegone with -4.72 kcal/ mol, which is better than that of thymoquinone (-4.54 kcal/ mol). A strong hydrogen bonding interaction with amino acid TRP207 (H-donor) along with four hydrophobic interactions with amino acid residues PHE3, LYS5, PHE291, and PHE291 are shown by camphor against host 6lu7 protease of SARS-CoV-2. In the formation process of protein-ligand complexes, hydrogen bonding plays a vital role in determining its specificity and affinity of complexes. Hydrogen bonding and hydrophobic interactions play an important role in giving shape and stabilizing the docked complex. Apart from describing binding energy, comparison of inhibition constant (Ki) can also give the information about inhibitor potential. Here, pulegone with 2gtb shows best Ki value of 45.30 µM in comparison to thymoquinone (57.70 µM) and camphor (66.18 µM). On the other, in case of 6lu7 target, camphor has the least value of inhibitory concentration of 247.69 µM, which is better than that of pulegone (347.49 µM) and thymoquinone (493.47 µM). The hydrogen bonding and hydrophobic interaction between the selected keto-based ligands and the host 6lu7 are summarized in Table 1. The 3D binding poses, which is elucidated using Protein-Ligand Interaction Profiler (PLIP) server, and further visualised via ligplot tool to determine the 2D structure for more simplification (Figure 1).

Furthermore, the docked ligands with hydrophobic interactions that are selected for identifying suitable binding pocket(s) around the ligands are summarized via Protein. Plus, server. This hydrophobic interaction between the ligands and the protein of host gives the information about the stability of the docked complex. The binding pocket of each ligand is shown in Figure 2, where different colors have been used for better visualization and easy investigation. PLIP outcomes are summarized in Figure 3, which provide not only the 3D plot but also the data related to hydrophobic interaction, amino acid involved in the interaction, and residue of that specific amino acid. Apart from that, hydrogen bonding and hydrophobic interactions plays an important role in giving shape and stabilizing the docking complex. From the results, it can be concluded that the number of hydrogen bonds as well as hydrophobic interactions are proportional to the activity of the corresponding ligand.




CCDC Gold has been used to corroborate the Autodock results by selecting all the required parameters like torsion angle distribution, rotatable bonds, protonated ligand, flexibility, and GA setting to run the programme. It provides us the binding sites along with number of solutions as represented in Figure 4. The results can be analyzed by comparing the bond distances from the functional group(s) of ligands to the amino acid residue of a specific protease of the host SARS-CoV-2. For example, pulegone interacts with a lowest bond distance of 1.949 Å with THR168 amino acid residue of host 2gtb and that of camphor is 1.748 Å against host 6lu7, respectively. The host-guest bond distance also plays important role for binding interaction and it is observed that less the bond distance between the host and guest molecule more is the activity. By using Chimera tool, 3D structure of host-guest complex of best docked analogue was elucidated to determine the active binding sites of ligand with protein (Figure 4).

Concepts of DFT

Initially, the geometry optimized solid state structures of the keto-based ligands are calculated through density functional theory (DFT). Also, molecular orbital energies like HOMO (EHOMO) and LUMO (ELUMO) were also calculated for those ligands to check their tendency to donate and/ or accept electrons towards protease hosts viz. 2gtb and 6lu7, respectively. The electron density in different regions of the molecule at HOMO and LUMO are generated and visualized in Figure 5. Furthermore, the HOMO energy (EHOMO) and LUMO energy (ELUMO) values of the selected natural drugs are summarized in Table 2. Besides, the electron density maps of molecular orbitals of the chosen keto-based ligands are shown in Figure 5, and energy or band gap (ΔE) between two molecular orbitals of the ligand is calculated with the formula: ΔE = ELUMO - EHOMO.

The energy gap that is directly proportional to the reactivity of a molecule, can be correlated to the transition from HOMO to LUMO of a molecule [42]. As represented in Table 2, thymoquinone shows least band gap of ΔE = 3.82 eV between HOMO and LUMO with respect to camphor (ΔE = 5.9 eV), and pulegone (ΔE = 5.22 eV). From this band gap value, it can be concluded that thymoquinone is more stable than pulegone and camphor and should be chemically less reactive. From the docking score values, it is also observable that thymoquinone has poor binding energy value, thus, less preferable to use as antiviral drug.



 
Further, the ionization potential and electron affinity can be calculated by using formulas I= -EHOMO and A= -ELUMO respectively. We also derived the values of electro negativity (χ), absolute hardness (η) and fraction of electron transfer (ΔN) by using the formula χ = (I+A)/2, η = (I-A)/2 and ΔN = χFeinh/2(ηFeinh), respectively. We considered the theoretical value of χFe =0 7eV and ηFe =0 eV to calculate ΔN value [16].

Again, we know that the molecular dipole moment is directly proportional to chemical reactivity [33]. Guest compounds that are taken for this study like thymoquinone, camphor, and pulegone gives molecular dipole moment of 0.2268 Debye, -3.3462 Debye, and 3.1750 Debye, respectively, which displays the best for camphor. This is exactly matching with the outcome of docking analysis as camphor shows the best activity towards host 6lu7.

CONCLUSION

Covid-19 is a viral respiratory infection caused by corona virus, which should be regulated to prevent further spread and mortality. The natural organic ligands found in various plants with potential antimicrobial or antifungal activities have been chosen for this study. Thus, these ligands can play a major role in preventing the recurrence of the virus in the gripping system and thus stopping further damage. Finding a suitable binding position inside the host protein(s) is very important to fix its viral action. Therefore, molecular docking simulation is of great interest of current research to select site specific drugs with targeted role. With the incorporation of DFT approach, this study has provided better understanding towards the chemical nature of keto-based natural drugs through defining electron density of molecules. At a glance, the outcomes of this in silico techniques using virtual examination can be very useful to find some phyto-compounds or natural drugs suitable for the treatment of Covid-19 viral infection.
 
 
DECLARATION OF COMPETING INTEREST

The authors declare no conflict of interest.

ACKNOWLEDGEMENT

Dr. B.K.K sincerely thanks to Prof. Dipankar Bhattacharyay, Research Center Coordinator of New Materials Group, School of Applied Sciences, CUTM Odisha, for his kind support throughout manuscript drafting and computational analysis.
  1. Wu F, Zhao S, Yu B, Chen YM, Wang W, et al. (2020) A new coronavirus associated with human respiratory disease in China. Nature 579: 265-269.
  2. Zhu N, Zhang D, Wang W, Li X, Yang B, et al. (2020) A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 382(8): 727-733.
  3. Hui DS, Azhar EI, Madani TA, Ntoumi F, Kock R, et al. (2020) The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health - The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis 91: 264-266.
  4. WHO (2020) Mental health and psychosocial considerations during the COVID-19 outbreak. Available online at: https://www.who.int/docs/default-source/coronaviruse/mental-health-considerations.pdf
  5. Swamy MK, Akhtar MS, Sinniah UR (2016) Antimicrobial properties of plant essential oils against human pathogens and their mode of action: An updated review. Evid Based Complement Alternat Med 2016: 3012462.
  6. Valdivieso-Ugarte M, Gomez-Llorente C, Plaza-Díaz J, Gil A (2019) Antimicrobial, antioxidant, and immunomodulatory properties of essential oils: A systematic review. Nutrients 11(11): 2786.
  7. Pandey AK, Kumar P, Singh P, Tripathi NN, Bajpai VK (2017) Essential oils: Sources of antimicrobials and food preservatives. Front Microbiol 7: 2161.
  8. Pourghanbari G, Nili H, Moattari A, Mohammadi A, Iraji A (2016) Antiviral activity of the oseltamivir and Melissa officinalis L. essential oil against avian influenza A virus (H9N2). Virus Dis 27: 170-178.
  9. Tariq S, Wani S, Rasool W, Shafi K, Bhat MA, et al. (2019) A comprehensive review of the antibacterial, antifungal and antiviral potential of essential oils and their chemical constituents against drug-resistant microbial pathogens. Microb Pathog 134: 103580.
  10. Srivastava P, Tiwari A (2017) Critical role of computer simulations in drug discovery and development. Curr Top Med Chem 17: 2422-2432.
  11. Ekins S, Mestres J, Testa B (2007) In silico pharmacology for drug discovery: Methods for virtual ligand screening and profiling. Br J Pharmacol 152: 9-20.
  12. Terstappen GC, Reggiani A (2001) In silico research in drug discovery. Trends Pharmacol Sci 22: 23-26.
  13. Kundu BK, Pragti, Mobin SM, Mukhopadhyay S (2020) Studies on the influence of the nuclearity of zinc(ii) hemi-salen complexes on some pivotal biological applications. Dalton Trans 49: 15481-15503.
  14. Majumdar D, Philip JE, Das S, Kundu BK, Saini RV, et al. (2021) Experimental and theoretical corroboration of antimicrobial and anticancer activities of two pseudohalides induced structurally diverse Cd (II)-Salen complexes. J Mol Struct 1225: 129189.
  15. Das M, Biswas A, Kundu BK, Charmier MAJ, Mukherjee A, et al. (2019) Enhanced pseudo-halide promoted corrosion inhibition by biologically active zinc (II) Schiff base complexes. Chem Eng J 357: 447-457.
  16. Das M, Biswas A, Kundu BK, Mobin S, Udayabhanu G, et al. (2017) Targeted synthesis of cadmium(ii) Schiff base complexes towards corrosion inhibition on mild steel. RSC Adv 7: 48569-48585.
  17. Das M, Kundu BK, Tiwari R, Mandal P, Nayak D, et al. (2018) Investigation on chemical protease, nuclease and catecholase activity of two copper complexes with flexidentate Schiff base ligands. Inorg Chim Acta 469: 111-122.
  18. Kundu BK (2019) Studies on metal complexes of 'N, O-Donor' ligands in modelling metalloenzymes sensing and catalysis, PhD Thesis, IIT Indore.
  19. Mandal P, Kundu BK, Vyas K, Sabu V, Helen A, (2018) Ruthenium(ii) arene NSAID complexes: inhibition of cyclooxygenase and antiproliferative activity against cancer cell lines. Dalton Trans 47: 517-527.
  20. Mandal P, Malviya N, Kundu BK, Singh DS, Nagaraja CM, et al. (2017) RAPTA complexes containing N‐substituted Tetrazole scaffolds: Synthesis, characterization and Antiproliferative activity. Appl Organomet Chem 32: 4179-4191.
  21. Kundu BK, Chhabra V, Malviya N, Ganguly R, Mishra GS, et al. (2018) Zeolite encapsulated host-guest Cu(II) Schiff base complexes: Superior activity towards oxidation reactions over homogenous catalytic systems. Micropor Mesopor Mat 271: 100-117.
  22. Kundu BK, Das M, Ganguly R, Bhobe PA, Mukhopadhyay S (2020) Role of zeolite encapsulated Cu(II) complexes in electron transfer as well as peroxy radical intermediates formation during oxidation of thioanizole. J Catal 389: 305-316.
  23. Kundu BK, Pragti, Biswas S, Mondal A, Mazumdar S, et al. (2021) Unveiling the urease like intrinsic catalytic activities of two dinuclear nickel complexes towards the in-situ syntheses of amino cyanopyridines, Dalton Trans.
  24. Kundu BK, Ranjan R, Mukherjee A, Mobin SM, Mukhopadhyay S (2019) Mannich base Cu(II) complexes as biomimetic oxidative catalyst. J Inorg Biochem 195: 164-173.
  25. Chhabra V, Kundu BK, Ranjan R, Pragti, Mobin SM, et al. (2020) Coligand driven efficiency of catecholase activity and proteins binding study of redox active copper complexes. Inorg Chim Acta 502: 119389.
  26. Kundu BK, Mandal P, Mukhopadhyay BG, Tiwari R, Nayak D, et al. (2019) Substituent dependent sensing behavior of Schiff base chemosensors in detecting Zn2+and Al3+ ions: Drug sample analysis and living cell imaging. Sens Actuators B: Chem 282: 347-358.
  27. Kundu BK, Pragti, Reena, Mobin SM, Mukhopadhyay S (2019) Mechanistic and thermodynamic aspects of a pyrene-based fluorescent probe to detect picric acid. New J Chem 43: 11483-11492.
  28. Kundu BK, Singh R, Tiwari R, Nayak D, Mukhopadhyay S (2019) An amide probe as a selective Al3+ and Fe3+ sensor inside the HeLa and a549 cell lines: Pictet-Spengler reaction for the rapid detection of tryptophan amino acid. New J Chem 43: 4867-4877.
  29. Zhang L, Lin D, Sun X, Curth U, Drosten C, et al. (2020) Crystal structure of SARS-CoV-2 main protease provides a basis for design of improved α-ketoamide inhibitors. Science 368: 409-412.
  30. Lan J, Ge J, Yu J, Shan S, Zhou H, et al. (2020) Structure of the SARS-CoV-2 spike receptor-binding domain bound to the ACE2 receptor. Nature 581: 215-220.
  31. Xu Z, Peng C, Shi Y, Zhu Z, Mu K, et al. (2020) Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation, BioRxiv.
  32. Walls AC, Park Y-J, Tortorici MA, Wall A, McGuire AT, et al. (2020) Structure, function, and antigenicity of the SARS-CoV-2 spike glycoprotein. Cell 181: 281-292.
  33. Trott O, Olson A (2010) AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J Comput Chem 31: 455-461.
  34. Dallakyan S, Olson AJ (2015) Small-molecule library screening by docking with PyRx, in: Chemical biology, Springer. pp: 243-250.
  35. O'Boyle NM, Banck M, James CA, Morley C, Vandermeersch T, et al. (2011) Open Babel: An open chemical toolbox. J Chem Inform 3: 1-14.
  36. Geerlings P, Chamorro E, Chattaraj PK, De Proft F, Gázquez JL, et al. (2020) Conceptual density functional theory: Status, prospects, issues. Theor Chem Acc 139: 1-18.
  37. Leach AR, Shoichet BK, Peishoff C (2006) Prediction of protein - ligand interactions. Docking and scoring: Successes and gaps. J Med Chem 49: 5851-5855.
  38. Vieira TF, Sousa SF (2019) Comparing AutoDock and Vina in ligand/decoy discrimination for virtual screening. Appl Sci 9: 4538.
  39. Elfiky AA, Ismail AM, Elshemey WM (2020) Recognition of gluconeogenic enzymes; Icl1, Fbp1, and Mdh2 by Gid4 ligase: A molecular docking study. J Mol Recognit 33: e2831.
  40. Leach AR (2001) Molecular modelling: Principles and applications. Pearson Education.
  41. Elfiky AA (2020) Novel guanosine derivatives against Zika virus polymerase in silico. J Med Virol 92: 11-16.
  42. Bostan R, Varvara S, Găină L, Mureşan LM (2012) Evaluation of some phenothiazine derivatives as corrosion inhibitors for bronze in weakly acidic solution. Corros Sci 63: 275-286.