In Silico Identification of Active Phytochemicals against COVID-19 by Targeting the SARS-CoV-2 Spike Glycoprotein Through Molecular Docking: A Drug Repurposing Approach

2021 ◽  
Vol 8 (2) ◽  
pp. 72-87
Author(s):  
Rathan Kumar
Keyword(s):  
Molecular Docking ◽  
Antiviral Treatment ◽  
Drug Repurposing ◽  
Modern Human ◽  
Binding Affinities ◽  
In Silico Identification ◽  
Adme Properties ◽  
Approved Drugs ◽  
Fda Approved Drugs ◽  
Natural Inhibitors

The spread of coronavirus disease (COVID-19) has become one of the most significant pandemics in modern human history, affecting more than 70 million people worldwide. Currently, only a few fda-approved drugs have suggested fighting the infection, in the absence of a specific antiviral treatment. Thus, repurposing the presently available drugs or using plant-based bioactive compounds can be the fastest possible solution. In this study, the computational methodology of molecular docking techniques was performed to screen and identify the viable potent inhibitors against the SARS-CoV-2 spike protein from a library of 200 active phytochemicals, based on their highest binding affinity towards the target protein. Later, the binding affinities of these phytochemicals were compared with that of the fda-approved drug fluvoxamine, which is currently in use against the mild COVID-19 patients. Out of these, 86 phytochemicals that exhibited better binding energy of value ≤-7.00kcal/mol, is selected for adme (absorption, distribution, metabolism, and excretion) analysis and drug likeliness studies to check the feasibility of these compounds. Wherein, 79 out of 86 phytochemicals showed a better theoretical affinity with sufficiently bearable adme properties. Thus, they can be the lead molecule for further investigation and validation processes towards developing natural inhibitors against the SARS-CoV-2 virus.

scholarly journals Multi-Targeted Approaches and Drug Repurposing Reveal Possible SARS-CoV-2 Inhibitors

Vaccines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 24
Author(s):  
Khalid Mashay Alanazi ◽  
Mohammad Abul Farah ◽  
Yan-Yan Hor
Keyword(s):  
Experimental Validation ◽  
Computational Analysis ◽  
Treatment Options ◽  
Drug Repurposing ◽  
Binding Affinities ◽  
Normal Model ◽  
Inhibitory Potential ◽  
Us Fda ◽  
Approved Drugs ◽  
Fda Approved Drugs

The COVID-19 pandemic caused by SARS-CoV-2 is unprecedented in recent memory owing to the non-stop escalation in number of infections and deaths in almost every country of the world. The lack of treatment options further worsens the scenario, thereby necessitating the exploration of already existing US FDA-approved drugs for their effectiveness against COVID-19. In the present study, we have performed virtual screening of nutraceuticals available from DrugBank against 14 SARS-CoV-2 proteins. Molecular docking identified several inhibitors, two of which, rutin and NADH, displayed strong binding affinities and inhibitory potential against SARS-CoV-2 proteins. Further normal model-based simulations were performed to gain insights into the conformational transitions in proteins induced by the drugs. The computational analysis in the present study paves the way for experimental validation and development of multi-target guided inhibitors to fight COVID-19.

scholarly journals Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

2020 ◽  
Author(s):  
Muhammad Umer Anwar ◽  
Farjad Adnan ◽  
Asma Abro ◽  
Muhammad Rayyan Khan ◽  
Asad Ur Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Docking ◽  
Active Sites ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Penicillin G ◽  
Computational Approaches ◽  
Docking Simulations ◽  
Approved Drugs ◽  
Fda Approved Drugs

<p></p><p>The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.</p><p></p>

scholarly journals Simeprevir and Eltrombopag as Potential Inhibitors of SARS-CoV2 Proteases: A Molecular Docking and Virtual Screening Approach to Combat COVID-19

2020 ◽  
Author(s):  
Ananta Swargiary ◽  
AKALESH Verma ◽  
Manita Daimari ◽  
Mritunjoy Kumar Roy
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Binding Affinities ◽  
Screening Approach ◽  
Approved Drugs ◽  
Potential Inhibitors ◽  
Fda Approved Drugs ◽  
Virtual Screening Approach

The present study investigates the binding affinities of 61 FDA approved drugs against two key proteases of SARS-COV2, 3-chymotrypsin-like protease and papain-like protease. We also investigates the ADMET properties of the top 10 besting binding drugs to understand the drug likeness property.

scholarly journals Combined Deep Learning and Molecular Docking Simulations Approach Identifies Potentially Effective FDA Approved Drugs for Repurposing Against SARS-CoV-2

2020 ◽  
Author(s):  
Muhammad Umer Anwar ◽  
Farjad Adnan ◽  
Asma Abro ◽  
Muhammad Rayyan Khan ◽  
Asad Ur Rehman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Molecular Docking ◽  
Active Sites ◽  
Drug Repurposing ◽  
Viral Proteins ◽  
Penicillin G ◽  
Computational Approaches ◽  
Docking Simulations ◽  
Approved Drugs ◽  
Fda Approved Drugs

<p></p><p>The ongoing pandemic of Coronavirus Disease 2019 (COVID-19), the disease caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has posed a serious threat to global public health. Currently no approved drug or vaccine exists against SARS-CoV-2. Drug repurposing, represented as an effective drug discovery strategy from existing drugs, is a time efficient approach to find effective drugs against SARS-CoV-2 in this emergency situation. Both experimental and computational approaches are being employed in drug repurposing with computational approaches becoming increasingly popular and efficient. In this study, we present a robust experimental design combining deep learning with molecular docking experiments to identify most promising candidates from the list of FDA approved drugs that can be repurposed to treat COVID-19. We have employed a deep learning based Drug Target Interaction (DTI) model, called DeepDTA, with few improvements to predict drug-protein binding affinities, represented as KIBA scores, for 2,440 FDA approved and 8,168 investigational drugs against 24 SARS-CoV-2 viral proteins. FDA approved drugs with the highest KIBA scores were selected for molecular docking simulations. We ran docking simulations for 168 selected drugs against 285 total predicted and/or experimentally proven active sites of all 24 SARS-CoV-2 viral proteins. We used a recently published open source AutoDock based high throughput screening platform virtualflow to reduce the time required to run around 50,000 docking simulations. A list of 49 most promising FDA approved drugs with best consensus KIBA scores and AutoDock vina binding affinity values against selected SARS-CoV-2 viral proteins is generated. Most importantly, anidulafungin, velpatasvir, glecaprevir, rifabutin, procaine penicillin G, tadalafil, riboflavin 5’-monophosphate, flavin adenine dinucleotide, terlipressin, desmopressin, elbasvir, oxatomide, enasidenib, edoxaban and selinexor demonstrate highest predicted inhibitory potential against key SARS-CoV-2 viral proteins.</p><p></p>

scholarly journals In Silico Molecular Docking and Ab initio DFT Studies as a Tool for a Quick Screening on Drug Inhibitors for SARS-Cov-2 RNA-Dependent Polymerase

2020 ◽  
Author(s):  
Manos C. Vlasiou ◽  
Kyriakos I. Ioannou ◽  
Kyriaki S. Pafiti
Keyword(s):  
Clinical Trials ◽  
Molecular Docking ◽  
Side Effects ◽  
In Silico ◽  
Binding Affinities ◽  
Dft Studies ◽  
Approved Drugs ◽  
In Silico Molecular Docking ◽  
Fda Approved Drugs ◽  
Ab Initio Dft

Abstract In silico molecular docking took place in order to evaluate already FDA approved drugs for several diseases, as inhibitors for SARSCov-2 RNA- dependent polymerase. The best candidates with the highest binding affinities, evaluated for their energy determined by their electron density. Remdesivir and Saquinavir seemed to be good candidates for clinical trials but their predicted toxicity based on their calculated structures revealed that these two substances should evaluated more for their side effects

scholarly journals Simeprevir and Eltrombopag as Potential Inhibitors of SARS-CoV2 Proteases: A Molecular Docking and Virtual Screening Approach to Combat COVID-19

2020 ◽  
Author(s):  
Ananta Swargiary ◽  
AKALESH Verma ◽  
Manita Daimari ◽  
Mritunjoy Kumar Roy
Keyword(s):  
Molecular Docking ◽  
Virtual Screening ◽  
Binding Affinities ◽  
Screening Approach ◽  
Approved Drugs ◽  
Potential Inhibitors ◽  
Fda Approved Drugs ◽  
Virtual Screening Approach

The present study investigates the binding affinities of 61 FDA approved drugs against two key proteases of SARS-COV2, 3-chymotrypsin-like protease and papain-like protease. We also investigates the ADMET properties of the top 10 besting binding drugs to understand the drug likeness property.

Structural Basis of Antisickling Effects of Selected FDA Approved Drugs: A Drug Repurposing Study

2018 ◽  
Vol 14 (2) ◽  
pp. 106-116 ◽  
Author(s):  
Olujide O. Olubiyi ◽  
Maryam O. Olagunju ◽  
James O. Oni ◽  
Abidemi O. Olubiyi
Keyword(s):  
Drug Repurposing ◽  
Structural Basis ◽  
Approved Drugs ◽  
Fda Approved Drugs

scholarly journals Screening of an FDA-Approved Library for Novel Drugs against Y. pestis

Antibiotics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 40
Author(s):  
David Gur ◽  
Theodor Chitlaru ◽  
Emanuelle Mamroud ◽  
Ayelet Zauberman
Keyword(s):  
Drug Repurposing ◽  
Mortality Rates ◽  
Systematic Screening ◽  
Antibiotic Resistant ◽  
Gram Negative ◽  
Therapy Initiation ◽  
Novel Drugs ◽  
Resistant Strains ◽  
Approved Drugs ◽  
Fda Approved Drugs

Yersinia pestis is a Gram-negative pathogen that causes plague, a devastating disease that kills millions worldwide. Although plague is efficiently treatable by recommended antibiotics, the time of antibiotic therapy initiation is critical, as high mortality rates have been observed if treatment is delayed for longer than 24 h after symptom onset. To overcome the emergence of antibiotic resistant strains, we attempted a systematic screening of Food and Drug Administration (FDA)-approved drugs to identify alternative compounds which may possess antibacterial activity against Y. pestis. Here, we describe a drug-repurposing approach, which led to the identification of two antibiotic-like activities of the anticancer drugs bleomycin sulfate and streptozocin that have the potential for designing novel antiplague therapy approaches. The inhibitory characteristics of these two drugs were further addressed as well as their efficiency in affecting the growth of Y. pestis strains resistant to doxycycline and ciprofloxacin, antibiotics recommended for plague treatment.

In Silico Modeling of FDA-Approved Drugs for Discovery of Therapies Against Neglected Diseases: A Drug Repurposing Approach

2019 ◽  
pp. 625-648 ◽  
Author(s):  
Carolina L. Belllera ◽  
María L. Sbaraglini ◽  
Lucas N. Alberca ◽  
Juan I. Alice ◽  
Alan Talevi
Keyword(s):  
In Silico ◽  
Drug Repurposing ◽  
Neglected Diseases ◽  
In Silico Modeling ◽  
Approved Drugs ◽  
Fda Approved Drugs
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