|
| Title | Reference | Method/software or databases applied | Drug target | Lead candidate | Experimental technique |
|
| Anti-COVID-19 activity of some benzofused 1,2,3-triazolesulfonamide hybrids using in silico and in vitro analyses | Alzahrani et al. [13] | Chemical synthesis/Cu(I)-catalyzed click 1,3-dipolar cycloaddition reaction | RNA-dependent RNA polymerase | Bis-(1,2,3-triazole-sulfadrug hybrids) carrying benzimidazole moiety (4b and 4c) against RNA-dependent RNA polymerase | In vitro antiviral activity |
| Molecular docking/MOE 2019 | Spike protein S1 main protease (3CLpro) | 4c against SARS-CoV-2 spike protein |
| Physicochemical properties and drug-likeness test/molinspiration and Mol-Soft software | 2′-O-methyltransferase (nsp16) | 4b and 4c against SARS-CoV-2 3CLpro and nsp16 |
| Chemical-informatics approach to COVID-19 drug discovery: the exploration of important fragments and data mining based prediction of some hits from natural origins as main protease (Mpro) inhibitors | Ghosh et al. [14] | QSAR/SiRMS tools | SARS-CoV-2 Mpro | Diazole, furan, and pyridine | None |
|
| Computational investigation of potent inhibitors against SARS-CoV-2 2′-O-methyltransferase (nsp16): structure-based pharmacophore modeling, molecular docking, molecular dynamics simulations, and binding free energy calculations | Shi et al. [15] | Pharmacophore modeling/phase | SARSCoV-2 2′-O-methyltransferase (nsp16) | C1 with CAS ID 1224032-33-0 and C2 with CAS ID 1224020-56-7 | None |
| Pharmacophore-based virtual screening/phase |
| Molecular docking/glide |
| Molecular dynamics simulation/Gromacs 2021 |
|
| Discovery of new drug indications for COVID-19: A drug repurposing approach | Kumari et al. [16] | Chemical-chemical and chemical-protein interaction/STITCH database | SARS-CoV-2 Mpro | Doxorubicin and buedesonide (pulmicort) | None |
| Randomization test/SWISSADME |
| Molecular docking/Autodock 4 tool |
|
| Discovery of novel TMPRSS2 inhibitors for COVID-19 using in silico fragment-based drug design, molecular docking, molecular dynamics, and quantum mechanics studies | Alzain et al., [17] | Homology modeling using Schrodinger | TMPRSS2 | Combine 1, 2, and 3 | None |
| Program |
| High-throughput virtual screening |
| Molecular docking |
| Molecular dynamics simulation |
|
| Exploring the treatment of COVID-19 with Yinqiao powder based on network pharmacology | Lin et al., [18] | Virtual screening | SARS-CoV-2 | Yinqiao powder | SPR assay |
| Protein-protein interaction network construction |
| Molecular docking |
|
| High-throughput screening identifies established drugs as SARS-CoV-2 PLpro inhibitors | Zhao et al., [19] | Virtual screening | SARS-CoV-2 papain-like protease (PLpro) | YM155 | Cell-based assays |
| SARS-CoV-2 main protease |
|
| In silico drug discovery of major metabolites from spices as SARS-CoV-2 main protease inhibitors | Ibrahim et al., [20] | Molecular docking | SARS-CoV-2 main protease | Salvianolic acid A and curcumin | None |
| Molecular dynamics simulation |
| Drug-likeness |
| Protein-protein interaction |
|
| In silico evaluation of prospective anti-COVID-19 drug candidates as potential SARS-CoV-2 main protease inhibitors | Ibrahim et al., [21] | Molecular docking | SARS-CoV-2 main protease | TMC-310911 and ritonavir | None |
| Molecular dynamics simulation |
|
| In silico investigation of ACE2 and the main protease of SARS-CoV-2 with phytochemicals from Myristica fragrans (Houtt.) for the discovery of a novel COVID-19 drug | Ongtanasup et al., [22] | Molecular docking | ACE2 and the main protease of SARS-CoV-2 | Myristica fragrans compounds | None |
| Molecular dynamics simulation |
| Drug-likeness and absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction |
|
| In silico screening of natural products isolated from Mexican herbal medicines against COVID-19 | Rivero-Segura and Gomez-Verjan [23] | Virtual screening | SARS-CoV-2 proteins | Cichoriin | None |
| Molecular docking |
| Pharmacokinetic assessment |
|
| In silico screening of novel TMPRSS2 inhibitors for treatment of COVID-19 | Wang et al., [24] | Homology modeling and virtual screening | TMPRSS2 | Lumacaftor and ergotamine | None |
| Molecular dynamics simulation |
|
| In silico screening of potential anti-COVID-19 bioactive natural constituents from food sources by molecular docking | Xu et al., [25] | Virtual screening | SARS-CoV-2 CLpro | Red wine, Chinese hawthorn, and blackberry | None |
| Molecular docking | Humans ACE2 |
| ADME analysis |
| Drug likeness |
|
| Inhibitory activity of FDA-approved drugs cetilistat, abiraterone, diiodohydroxyquinoline, bexarotene, remdesivir, and hydroxychloroquine on COVID-19 main protease and human ACE2 receptor: A comparative in silico approach | Shahabadi et al., [26] | Molecular docking | SARS-CoV-2 main protease | Cetilistat, abiraterone, di-iodo hydroxyquinoline, and bexarotene | None |
| Molecular dynamics simulation | ACE2 |
|
| In-silico drug repurposing and molecular dynamics puzzled out potential SARS-CoV-2 main protease inhibitors | Ibrahim et al., [27] | Molecular docking | SARS-CoV-2 main protease | DB02388 and cobicistat | None |
| Molecular dynamics simulation |
|
| Investigating the active compounds and mechanism of HuaShi XuanFei formula for prevention and treatment of COVID-19 based on network pharmacology and molecular docking analysis | Wang et al., [28] | Virtual screening | 3C-like (3CL) protease hydrolase and angiotensin-converting enzyme 2 (ACE2) | HuaShi XuanFei | None |
| Molecular interaction networks using Cytoscape | Formula (HSXFF) |
| Protein–protein interaction (PPI) network construction |
| Gene ontology enrichment analysis and KEGG pathway analysis |
| Molecular docking |
| Molecular dynamic (MD) simulation |
|
| Luteolin and abyssinone II as potential inhibitors of SARS-CoV-2: an in silico molecular modeling approach in battling the COVID-19 outbreak | Shawan et al., [29] | Creation of flavonoids library | ACE2 of human host and Mpro/3CLpro and PLpro of SARS-CoV-2 | Luteolin and abyssinone II | None |
| Drug likeness/pharmacophore and ADMET profile analysis |
| Virtual screening and molecular docking |
| Molecular dynamics simulation |
| ADMET profile analysis |
|
| Marine algal antagonists targeting 3CL protease and spike glycoprotein of SARS-CoV-2: a computational approach for anti-COVID-19 drug discovery | Arunkumar et al., [30] | Molecular docking tools (AutoDockTools) | 3CL protease and spike glycoprotein of SARS-CoV-2 | k-Carrageenan, laminarin, eckol, trifucol, and b-D-galactose | None |
| Molecular dynamic simulation, ADMET, and density functional theory calculations |
|
| MCCS: a novel recognition pattern-based method for fast-track discovery of anti-SARS-CoV-2 drugs | Feng et al., [31] | Virtual screening by MCCS | 3CLPro in SARS-CoV-2 | Lopinavir, tenofovir disoproxil, fosamprenavir, and ganciclovir | None |
| Peramivir and zanamivir |
| Sofosbuvir |
|
| Molecules against Covid-19: an in silico approach for drug development | Bharti and Shukla [32] | Molecular docking | SARS-CoV-2 ribonucleic acid (RNA)-dependent RNA polymerase (RdRp) | Ellipticine, ecteinascidin, homo harringtonine, dolastatin 10, halichondrin, and plicamycin | None |
| Absorption, distribution, metabolism, and excretion (ADME) analysis |
| Drug-likeness test |
|
| Multidimensional in silico strategy for identification of natural polyphenols-based SARS-CoV-2 main protease (Mpro) inhibitors to unveil a hope against COVID-19 | Adem et al., [33] | Quantum mechanics | SARS-CoV-2 main protease (Mpro) | Hesperidin, rutin, diosmin, and apiin | None |
| Molecular docking |
| Molecular dynamic simulations |
|
| Multi-step in silico discovery of natural drugs against COVID-19 targeting main protease | Elkaeed et al., [34] | Molecular similarity detection using | SARS-CoV-2 main protease | Luteoside C, kahalalide E, and streptovaricin B | None |
| Discovery Studio software |
| Molecular fingerprint detection using |
| Discovery Studio software |
| Docking studies using MOE.14 software |
| Toxicity studies using discovery |
| Studio 4.0 |
| Molecular dynamics (MD) simulations using the GROningen MAchine |
|
| Natural-like products as potential SARS-CoV-2 Mpro inhibitors: in-silico drug discovery | Ibrahim et al., [35] | Virtual screening of MolPort database | SARS-CoV-2 Mpro | Four bis [1, 3] dioxolo pyran-5-carboxamide derivatives | None |
| Molecular docking |
| Molecular |
| Dynamics (MD) simulations |
| Drug-likeness predictions |
|
| Potent toxic effects of Taroxaz-104 on the replication of SARS-CoV-2 particles | Rabie [37] | Computational molecular docking studies | RNA-dependent RNA polymerase (nCoV-RdRp) | Taroxaz-104 | In vitro anti-COVID-19 bioactivities of Taroxaz-104 |
| In vitro anti-COVID-19 bioactivities of Taroxaz-104 |
|
| Promising terpenes as SARS-CoV-2 spike receptor-binding domain (RBD) attachment inhibitors to the human ACE2 receptor: an integrated computational approach | Muhseen et al., [38] | Structure-based virtual screening | SARS-CoV-2 spike receptor-binding domain (RBD) | NPACT01552, NPACT01557 and NPACT00631 | None |
| Molecular dynamics (MD) simulation |
|
| Rational design of potent anti-COVID-19 main protease drugs: an extensive multi-spectrum in silico approach | Ahmad et al., [36] | Structure-based virtual screening (SBVS) of ASINEX antiviral library | SARS-CoV-2 MPro | SCHEMBL 12616233, SCHEMBL 18616095, and SCHEMBL 20148701 | None |
| Drug-likeness and lead likeness annotations |
| Pharmacokinetics analysis |
| Molecular dynamics (MD) simulations |
|
| Rutin and flavone analogs as prospective SARS-CoV-2 main protease inhibitors: in silico drug discovery study | Ibrahim et al., [39] | Virtual screening | SARS-CoV-2 Mpro | PubChem-129-716-607 and pubChem-885-071-27 | None |
| Molecular docking |
| Molecular dynamics simulations |
| Drug-likeness evaluation |
|
| Screening, molecular simulation and in silico kinetics of virtually designed Covid-19 main protease inhibitors | Aleissa et al., [40] | Virtual screening | SARS-CoV-2 Mpro | HIT-1 and HIT-2 | None |
| Molecular docking |
| Molecular dynamics (MD) simulations |
| ADME calculations |
|
| Structure-based screening of natural product libraries in search of potential antiviral drug leads as first-line treatment for COVID-19 infection | Rao and Shetty [41] | Virtual screening | SARS-CoV NSP12 polymerase | 12,28-Oxa-8-hydroxy-manzamine A | None |
| Pharmacokinetic and pharmacodynamics properties analysis |
| Molecular docking |
| Molecular dynamic simulations |
|
| Targeting SARS-CoV-2 RNA-dependent RNA polymerase: an in silico drug repurposing for COVID-19 [version 1; peer review: 2 approved] | Baby et al., [42] | Molecular docking | SARS-CoV-2 RNA-dependent RNA polymerase | Pitavastatin, ridogrel, and rosoxacin | None |
| Molecular dynamics simulation |
|
| Targeting SARS-CoV-2 spike protein of COVID-19 with naturally occurring phytochemicals: an in silico study for drug development | Pandey et al., [43] | Molecular docking | SARS-CoV-2 spike protein | Fisetin, quercetin, and kaempferol | None |
| Molecular dynamics (MD) simulation |
| ADME analysis |
|
| The potential effects of clinical antidiabetic agents on SARS-CoV-2 | Qu et al., [44] | Molecular dynamics simulation | SARS-CoV-2 Mpro | Repaglinide, canagliflozin, glipizide, gliquidone, glimepiride, and linagliptin | In vitro study |
| Molecular docking study |
| In vitro study |
|
| Virtual screening-driven drug discovery of SARS-CoV2 enzyme inhibitors targeting viral attachment, replication, post-translational modification and host immunity evasion infection mechanisms | Quimque et al., [45] | Molecular docking | SARS-CoV2 PLpro | Three fumiquinazoline alkaloids scedapin C, quinadoline B, and norquinadoline A | None |
| Molecular dynamics simulation | Chymotrypsin-like protease (3CLpro) | The polyketide iso-chaetochromin |
| Drug-likeness, ADME, and toxicity prediction | SARS-CoV-2 RdRp | The terpenoid 11a-de hydroxy isoterreulactone A |
| SARS-CoV-2 nsp15 |
| SARS-CoV-2 S protein (spikes) |
|
