QSAR Modeling of SARS‐CoV Mpro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2

Alves, Vinícius M.; Bobrowski, Tesia; Melo-Filho, Cleber C.; Korn, Daniel; Auerbach, Scott S.; Schmitt, Charles; Muratov, Eugene; Tropsha, Alexander

doi:10.1002/minf.202000113

Cited by 68 publications

(51 citation statements)

References 55 publications

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“…QSAR models developed by us earlier were used for selection of drugs 40 , 41 that could be repurposed as combinations and exclusion of potential drug-drug interactions and side effects. 36 All models were developed according to the best practices of QSAR modeling, 34 , 42 , 43 with special attention paid for data curation 44 , 45 , 46 and rigorous external validation.…”

Section: Methodsmentioning

confidence: 99%

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

et al. 2021

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Section: Methodsmentioning

confidence: 99%

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

et al. 2021

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“…NCATS released, via OpenData Portal ( ) the quantitative HTS data on drugs approved for clinical use tested in the SARS-CoV-2 cytopathic effect (CPE) assay. We used the chemical structures and data of 3957 molecules in SMILES format of this SARS-CoV-2 CPE evaluation from the work of Alves et al [ 17 ]. From those 3957 molecules, there are 1401 molecules that were used to build (910 molecules in the training set) and validate (409 and 82 molecules in the test and test 2 sets, respectively) the SARS-CoV-2 QSAR model.…”

Section: Resultsmentioning

confidence: 99%

“…Several studies have been reported with the development of ligand-based CADD approaches for the discovery of inhibitors against SARS-CoV-2 [ 16 , 17 , 18 ]. Ghosh et al reported the development of several Monte Carlo optimization-based, quantitative structure–activity relationship (QSAR) models with a diverse dataset comprising 88 compounds with SARS-CoV-2 M pro assay from the ChEMBL database and the best model was used for virtual screening of 60 NPs from recent publications [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

Gaudêncio

Pereira

2020

Marine Drugs

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The investigation of marine natural products (MNPs) as key resources for the discovery of drugs to mitigate the COVID-19 pandemic is a developing field. In this work, computer-aided drug design (CADD) approaches comprising ligand- and structure-based methods were explored for predicting SARS-CoV-2 main protease (Mpro) inhibitors. The CADD ligand-based method used a quantitative structure–activity relationship (QSAR) classification model that was built using 5276 organic molecules extracted from the ChEMBL database with SARS-CoV-2 screening data. The best model achieved an overall predictive accuracy of up to 67% for an external and internal validation using test and training sets. Moreover, based on the best QSAR model, a virtual screening campaign was carried out using 11,162 MNPs retrieved from the Reaxys® database, 7 in-house MNPs obtained from marine-derived actinomycetes by the team, and 14 MNPs that are currently in the clinical pipeline. All the MNPs from the virtual screening libraries that were predicted as belonging to class A were selected for the CADD structure-based method. In the CADD structure-based approach, the 494 MNPs selected by the QSAR approach were screened by molecular docking against Mpro enzyme. A list of virtual screening hits comprising fifteen MNPs was assented by establishing several limits in this CADD approach, and five MNPs were proposed as the most promising marine drug-like leads as SARS-CoV-2 Mpro inhibitors, a benzo[f]pyrano[4,3-b]chromene, notoamide I, emindole SB beta-mannoside, and two bromoindole derivatives.

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“…From these, five compounds were shown to be inactive in the counter screen indicating that they were true positives in the CPA assay: umifenovir, imatinib, promethazine, fluoxetine, and reserpine. Umifenovir (arbidol) was also identified as active in the QSAR models for severe acute respiratory syndrome coronavirus (SARS-CoV) M pro our group described recently [20].…”

Section: Comparison Of the Drug-target Associations In The Coke Datasmentioning

confidence: 98%

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

Korn¹,

Pervitsky²,

Bobrowski³

et al. 2020

Preprint

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Objective: The COVID-19 pandemic has catalyzed a widespread effort to identify drug candidates and biological targets of relevance to SARS-COV-2 infection, which resulted in large numbers of publications on this subject. We have built the COVID-19 Knowledge Extractor (COKE), a web application to extract, curate, and annotate essential drug-target relationships from the research literature on COVID-19 to assist drug repurposing efforts. Materials and Methods: SciBiteAI ontological tagging of the COVID Open Research Dataset (CORD-19), a repository of COVID-19 scientific publications, was employed to identify drug-target relationships. Entity identifiers were resolved through lookup routines using UniProt and DrugBank. A custom algorithm was used to identify co-occurrences of protein and drug terms, and confidence scores were calculated for each entity pair. Results: COKE processing of the current CORD-19 database identified about 3,000 drug-protein pairs, including 29 unique proteins and 500 investigational, experimental, and approved drugs. Some of these drugs are presently undergoing clinical trials for COVID-19. Discussion: The rapidly evolving situation concerning the COVID-19 pandemic has resulted in a dramatic growth of publications on this subject in a short period. These circumstances call for methods that can condense the literature into the key concepts and relationships necessary for insights into SARS-CoV-2 drug repurposing. Conclusion: The COKE repository and web application deliver key drug - target protein relationships to researchers studying SARS-CoV-2. COKE portal may provide comprehensive and critical information on studies concerning drug repurposing against COVID-19. COKE is freely available at <a href="https://coke.mml.unc.edu/">https://coke.mml.unc.edu/</a> and the code is available at <a href="https://github.com/DnlRKorn/CoKE">https://github.com/DnlRKorn/CoKE</a>.

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QSAR Modeling of SARS‐CoV M^pro Inhibitors Identifies Sufugolix, Cenicriviroc, Proglumetacin, and other Drugs as Candidates for Repurposing against SARS‐CoV‐2

Cited by 68 publications

References 55 publications

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

Synergistic and Antagonistic Drug Combinations against SARS-CoV-2

A Computer-Aided Drug Design Approach to Predict Marine Drug-Like Leads for SARS-CoV-2 Main Protease Inhibition

COVID-19 Knowledge Extractor (COKE): A Tool and a Web Portal to Extract Drug - Target Protein Associations from the CORD-19 Corpus of Scientific Publications on COVID-19

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