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

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.

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Combined deep learning and molecular docking simulations approach identifies potentially effective FDA approved drugs for repurposing against SARS-CoV-2

Computers in Biology and Medicine ◽

10.1016/j.compbiomed.2021.105049 ◽

2021 ◽

pp. 105049

Author(s):

Muhammad U. Anwaar ◽

Farjad Adnan ◽

Asma Abro ◽

Rayyan A. Khan ◽

Asad U. Rehman ◽

...

Keyword(s):

Deep Learning ◽

Molecular Docking ◽

Docking Simulations ◽

Approved Drugs ◽

Fda Approved Drugs

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Target-Centered Drug Repurposing Predictions of Human Angiotensin-Converting Enzyme 2 (ACE2) and Transmembrane Protease Serine Subtype 2 (TMPRSS2) Interacting Approved Drugs for Coronavirus Disease 2019 (COVID-19) Treatment through a Drug-Target Interaction Deep Learning Model

Viruses ◽

10.3390/v12111325 ◽

2020 ◽

Vol 12 (11) ◽

pp. 1325

Author(s):

Yoonjung Choi ◽

Bonggun Shin ◽

Keunsoo Kang ◽

Sungsoo Park ◽

Bo Ram Beck

Keyword(s):

Deep Learning ◽

Angiotensin Converting Enzyme ◽

Drug Target ◽

Expression Profiles ◽

Drug Repurposing ◽

Converting Enzyme ◽

Angiotensin Converting Enzyme 2 ◽

Target Interaction ◽

Approved Drugs ◽

Fda Approved Drugs

Previously, our group predicted commercially available Food and Drug Administration (FDA) approved drugs that can inhibit each step of the replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) using a deep learning-based drug-target interaction model called Molecule Transformer-Drug Target Interaction (MT-DTI). Unfortunately, additional clinically significant treatment options since the approval of remdesivir are scarce. To overcome the current coronavirus disease 2019 (COVID-19) more efficiently, a treatment strategy that controls not only SARS-CoV-2 replication but also the host entry step should be considered. In this study, we used MT-DTI to predict FDA approved drugs that may have strong affinities for the angiotensin-converting enzyme 2 (ACE2) receptor and the transmembrane protease serine 2 (TMPRSS2) which are essential for viral entry to the host cell. Of the 460 drugs with Kd of less than 100 nM for the ACE2 receptor, 17 drugs overlapped with drugs that inhibit the interaction of ACE2 and SARS-CoV-2 spike reported in the NCATS OpenData portal. Among them, enalaprilat, an ACE inhibitor, showed a Kd value of 1.5 nM against the ACE2. Furthermore, three of the top 30 drugs with strong affinity prediction for the TMPRSS2 are anti-hepatitis C virus (HCV) drugs, including ombitasvir, daclatasvir, and paritaprevir. Notably, of the top 30 drugs, AT1R blocker eprosartan and neuropsychiatric drug lisuride showed similar gene expression profiles to potential TMPRSS2 inhibitors. Collectively, we suggest that drugs predicted to have strong inhibitory potencies to ACE2 and TMPRSS2 through the DTI model should be considered as potential drug repurposing candidates for COVID-19.

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Repurposing inhibitors for SARS-CoV2 main protease

10.21203/rs.3.rs-41342/v1 ◽

2020 ◽

Author(s):

Kumar Sharp

Keyword(s):

Viral Replication ◽

Active Sites ◽

Drug Repurposing ◽

Chemotherapeutic Drugs ◽

Short Term ◽

Main Protease ◽

Potential Binding ◽

Approved Drugs ◽

Fda Approved Drugs

Abstract SARS-CoV2 main protease is important for viral replication and one of the most potential targets for drug development in this current pandemic. Drug repurposing is a promising field to provide potential short-term acceptable therapy for management of coronavirus till a specific anti-viral for coronavirus is developed. In-silico drug repurposing screening is the current fastest way to repurpose drugs by targeting active sites in fraction of seconds. In this study, SARS-CoV2 main protease is being targeted by 1050 FDA-approved drugs to inhibit its activity thereby interfering with viral replication. Chemotherapeutic drugs and anti-retroviral drugs have shown potential binding as inhibitor. In-vitro and clinical trials required to establish final fact.

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

International Journal for Research in Applied Sciences and Biotechnology ◽

10.31033/ijrasb.8.2.10 ◽

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.

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Molecular docking, molecular dynamics and MM/PBSA studies of FDA approved drugs for protein kinase a of Mycobacterium tuberculosis; application insights of drug repurposing

Informatics in Medicine Unlocked ◽

10.1016/j.imu.2019.100210 ◽

2019 ◽

Vol 16 ◽

pp. 100210 ◽

Cited By ~ 2

Author(s):

Shobana Sundar ◽

Lokesh Thangamani ◽

Gowdham Manivel ◽

Praveen Kumar ◽

Shanmughavel Piramanayagam

Keyword(s):

Molecular Dynamics ◽

Mycobacterium Tuberculosis ◽

Molecular Docking ◽

Protein Kinase ◽

Protein Kinase A ◽

Drug Repurposing ◽

Approved Drugs ◽

Fda Approved Drugs ◽

Kinase A

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New Inhibitors of the P38 Mitogen-Activated Protein Kinase: Repurposing Existing Drugs with Deep Learning

Biointerface Research in Applied Chemistry ◽

10.33263/briac124.53845404 ◽

2021 ◽

Vol 12 (4) ◽

pp. 5384-5404

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Protein Kinase ◽

Inflammatory Diseases ◽

Mitogen Activated Protein Kinase ◽

Docking Simulations ◽

Mitogen Activated Protein ◽

Approved Drugs ◽

Fda Approved Drugs

The p38-alpha (MAPK14) is a protein kinase that is implicated in the pathological mechanisms of BAG3P209L myofibrillar myopathy, cancers, and inflammatory diseases like Alzheimer’s and rheumatoid arthritis. Inhibition of p38 has shown promise as a treatment for these diseases. Traditional drug discovery methods could not create effective and safe small molecule inhibitors, so we used machine learning to elucidate potential p38 blockers from existing FDA-approved drugs. Using PubChem bioactivity data, we determined the best existing p38 inhibitors and applied fingerprint clustering to isolate the compounds with similar structures. Descriptors were calculated for these clustered compounds, and the most important of these descriptors were determined through a machine learning-based feature selection algorithm. This data served as the training set for a deep neural network that was fine-tuned to a 92% validation accuracy. The neural network model was applied to a database of FDA-approved drugs, revealing 149 potential p38 inhibitors, whose efficacy was confirmed by docking simulations to be statistically significantly higher than random FDA drugs and slightly higher than known inhibitors. Our study not only reveals potential medications for p38-mediated diseases that we recommend for physical trials but also demonstrates the ability of our novel deep learning-based computational pipeline to predict new functions of existing pharmaceuticals.

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

Current Computer - Aided Drug Design ◽

10.2174/1573409914666180129163711 ◽

2018 ◽

Vol 14 (2) ◽

pp. 106-116 ◽

Cited By ~ 1

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

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

Antibiotics ◽

10.3390/antibiotics10010040 ◽

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.

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