Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning

Kadioglu, Onat; Saeed, Mohamed E.M.; Greten, Henry Johannes; Efferth, Thomas

doi:10.2471/blt.20.255943

Cited by 54 publications

(80 citation statements)

References 51 publications

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“…Considering an urgent need of containing the pandemic, repurposing of previously approved drugs to use against SARS-CoV-2 is the first choice in the fight against this virus, as such drugs are not required to pass through most of the steps of an extensive drug testing process. In this regard, several recent studies have been conducted using computational methods to screen libraries of approved drugs or drug-like molecules to identify potential inhibitors of different viral proteins, particularly, RdRp and 3CL-protease [13][14][15][16][17]. Moreover, high throughput in vitro screening of FDA approved drug libraries followed by in vivo validation has also been applied on limited hits [18,19].…”

Section: Introductionmentioning

confidence: 99%

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach

Iftikhar

Ali

Farooq

et al. 2020

Computers in Biology and Medicine

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The recent outbreak of coronavirus disease-19 (COVID-19) continues to drastically affect healthcare throughout the world. To date, no approved treatment regimen or vaccine is available to effectively attenuate or prevent the infection. Therefore, collective and multidisciplinary efforts are needed to identify new therapeutics or to explore effectiveness of existing drugs and drug-like small molecules against SARS-CoV-2 for lead identification and repurposing prospects. This study addresses the identification of small molecules that specifically bind to any of the three essential proteins (RdRp, 3CL-protease and helicase) of SARS-CoV-2. By applying computational approaches we screened a library of 4574 compounds also containing FDA-approved drugs against these viral proteins. Shortlisted hits from initial screening were subjected to iterative docking with the respective proteins. Ranking score on the basis of binding energy, clustering score, shape complementarity and functional significance of the binding pocket was applied to identify the binding compounds. Finally, to minimize chances of false positives, we performed docking of the identified molecules with 100 irrelevant proteins of diverse classes thereby ruling out the non-specific binding. Three FDA-approved drugs showed binding to 3CL-protease either at the catalytic pocket or at an allosteric site related to functionally important dimer formation. A drug-like molecule showed binding to RdRp in its catalytic pocket blocking the key catalytic residues. Two other druglike molecules showed specific interactions with helicase at a key domain involved in catalysis. This study provides lead drugs or drug-like molecules for further in vitro and clinical investigation for drug repurposing and new drug development prospects.

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Section: Introductionmentioning

confidence: 99%

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach

Iftikhar

Ali

Farooq

et al. 2020

Computers in Biology and Medicine

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“…Similarly, using BenevolentAI’s knowledge graph and a library of structured medical information, machine learning uncovered baricitinib, a Janus kinase inhibitor used for rheumatoid arthritis, as a safe candidate drug that could inhibit SARS-CoV-2 viral entry [ 27 ]. In another study, a similar process of virtual drug screening identified antiviral agents against hepatitis C as drugs that had high binding affinities to target proteins on SARS-CoV-2 [ 26 ].…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for COVID-19: Rapid Review

Chen¹,

See²

2020

J Med Internet Res

125

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Background COVID-19 was first discovered in December 2019 and has since evolved into a pandemic. Objective To address this global health crisis, artificial intelligence (AI) has been deployed at various levels of the health care system. However, AI has both potential benefits and limitations. We therefore conducted a review of AI applications for COVID-19. Methods We performed an extensive search of the PubMed and EMBASE databases for COVID-19–related English-language studies published between December 1, 2019, and March 31, 2020. We supplemented the database search with reference list checks. A thematic analysis and narrative review of AI applications for COVID-19 was conducted. Results In total, 11 papers were included for review. AI was applied to COVID-19 in four areas: diagnosis, public health, clinical decision making, and therapeutics. We identified several limitations including insufficient data, omission of multimodal methods of AI-based assessment, delay in realization of benefits, poor internal/external validation, inability to be used by laypersons, inability to be used in resource-poor settings, presence of ethical pitfalls, and presence of legal barriers. AI could potentially be explored in four other areas: surveillance, combination with big data, operation of other core clinical services, and management of patients with COVID-19. Conclusions In view of the continuing increase in the number of cases, and given that multiple waves of infections may occur, there is a need for effective methods to help control the COVID-19 pandemic. Despite its shortcomings, AI holds the potential to greatly augment existing human efforts, which may otherwise be overwhelmed by high patient numbers.

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“…This compound is FDA approved for use in adult patients infected with HIV-1. Nevirapine has also been revealed in various in-silico drug screening with the main protease 115,116 . Pentostatin is a purine analog that is widely used as a treatment for hairy cell leukemia 117 .…”

Section: Resultsmentioning

confidence: 99%

Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening and in vitro assays

Gupta

Maciorowski

Zak

et al. 2020

Preprint

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The emergence of SARS/MERS drug-resistant SARS-CoV2 comes with higher rates of transmission and mortality. Like all coronaviruses, SARS-CoV-2 is a relatively large virus consisting of several enzymes with essential functions within its proteome. Here, we focused on repurposing approved and investigational drugs by identifying potential drugs that are predicted to effectively inhibit critical enzymes. We targeted seven proteins with enzymatic activities known to be essential at different stages of the viral multiplication cycle including PLpro, 3CLpro, RdRP, Helicase, ExoN, NendoU, and 2’-O-MT. For virtual screening, the energy minimization of a crystal structure of the modeled protein was carried out using the Protein Preparation Wizard(Schrodinger LLC 2020-1). Following active site selection based on data mining and COACH predictions, we performed a high-throughput virtual screen of drugs (n=5903) that are approved by worldwide regulatory bodies. The screening was performed against viral targets using three sequential docking modes (i.e. HTVS, SP, and XP). Our in-silico virtual screening identified ~290 potential drugs based on the criteria of energy, docking parameters, ligand, and binding site strain and score. Drugs specific to each target protein were further analyzed for binding free energy perturbation by molecular mechanics (prime MM-GBSA) and pruning the hits to the top 32 candidates. The top lead from each target pool was further subjected to molecular dynamics simulation using the Desmond module. Herein we report the evaluation of in-vitro efficacy of selected hit drug molecules on SARS-CoV-2 inhibition. Among eight molecules included in our evaluation, we found inhibitor of protein kinase C isoforms, Bisindolylmaleimide IX (BIM IX), as the potent inhibitor of SARS-CoV-2 in-vitro. Further, in-silico predicted target validation through enzymatic assays confirmed 3CLpro to be the target. Therefore, our data support advancing BIM IX for clinical evaluation as a potential treatment for COVID-19. This is the first study that has showcased the possibility of using bisindolylmaleimide IX to treat COVID-19 through this pipeline.

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Identification of novel compounds against three targets of SARS CoV-2 coronavirus by combined virtual screening and supervised machine learning

Cited by 54 publications

References 51 publications

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach

Identification of potential inhibitors of three key enzymes of SARS-CoV2 using computational approach

Artificial Intelligence for COVID-19: Rapid Review

Bisindolylmaleimide IX: A novel anti-SARS-CoV2 agent targeting viral main protease 3CLpro demonstrated by virtual screening and in vitro assays

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