<scp>AVC</scp>pred: an integrated web server for prediction and design of antiviral compounds

Qureshi, Abid; Kaur, Guneet; Kumar, Manoj

doi:10.1111/cbdd.12834

Cited by 62 publications

(59 citation statements)

References 64 publications

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“…It become more important, especially for the viruses against whom no treatment modality is available. Although, the limited antiviral prediction algorihtms are available for the prediction of compounds against viral infections, which includes the AVCpred and HIVprotI [15,16]. The AVCpred is an antiviral compound prediction server, especially for viruses HIV, HCV, HBV, HHV and also, include a general prediction tool for 26 viruses.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, there is a need for a computational tool that can identify the unexplored putative inhibitor against NiV. We have previously developed the antiviral prediction servers mainly for Zika virus, Human immunodeficiency virus (HIV), Hepatitis B virus (HBV) and Hepatitis C virus (HCV) [15][16][17]. However, in the present study, we have collected the overall anti-nipah inhibitors available in the literature and developed first quantitative structure-activity relation (QSAR) based prediction algorithm using support vector machine learning for the identification of anti-NiV compounds along the data visualization modules.…”

Section: Introductionmentioning

confidence: 99%

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Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

Rajput¹,

Kumar²,

Kumar³

2018

Preprint

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Nipah virus (NiV) is responsible to cause various outbreaks in Asian countries, with latest from Kerala state of India. Till date there is no drug available despite its urgent requirement. In the current study, we have provided a computational one-stop solution for NiV inhibitors. We have developed “anti-Nipah” web resource, which comprised of a data repository, prediction method, and data visualization modules. The database comprised of 313 (181 unique) inhibitors from different strains and outbreaks of NiV extracted from research articles and patents. However, the quantitative structure–activity relationship (QSAR) based predictors were accomplished using classification approach employing 10-fold cross validation through support vector machine with 120 (68p + 52n) inhibitors. The overall predictor showed the accuracy and Matthew’s correlation coefficient of 88.89% and 0.77 on training/testing dataset respectively. The independent validation dataset also performed equally well. The data visualization modules from chemical clustering and principal component analyses displayed the diversity in the NiV inhibitors. Therefore, our web platform would be of immense help to the researchers working in developing effective inhibitors against NiV. The user-friendly webserver is freely available on URL: http://bioinfo.imtech.res.in/manojk/antinipah/

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

Rajput¹,

Kumar²,

Kumar³

2018

Preprint

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“…In the current face of tragic of unavailable clinically approved vaccines or drugs for SARS-CoV-2 treatment except few antiviral agents and others (remdesivir, favipiravir, hydroquinone, azithromycin, dexamethasone) to lessen the severity of complications of COVID-19 [10] paving a way to drugs/vaccine discovery is utmost. Antiviral agents could be discovered and developed by targeting, broadly various stages of the life cycle of virus (entry, attachment, replication, transcription, translation, maturation, release) [58,59] by currently available methods, either computational or/and traditional. And/or, targeting molecular targets such as ACE2 of human, 3CL pro protease, Nsp13 helicase of SARS-CoV-2, for instance [61,62].…”

Section: Identi Cation Of the Key Medical And Social Elementsmentioning

confidence: 99%

Spatial Distribution and Trend Analysis of Current Status of COVID-19 in Nepal and Global Future Preventive Perspectives

Pant

Bahadur

Bishwakarma

et al. 2020

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Background: Coronavirus disease (COVID-19) is a recently discovered severe and contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and has received worldwide attention. The risk of COVID-19 is serious for the infected persons of chronic diseases as well as vulnerable populations including elder group. Still, the present scenario of scarcity of effective treatment options and limited recovery rate of ongoing treatment are prevailed in Nepal. This study aims to analyze the spatial distribution and trends of COVID-19 with the help of geographic information system (GIS) software and outlook future preventive perspectives.Methods: In this research work, we used GIS tool ArcGIS 10.4.1 to map the distribution patterns of population of COVID-19 cases. Federal, provincial and district level daily cases data of COVID-19 confirmed, cured and death from 23rd January to 13th July 2020 were obtained from the Ministry of Health and Population (MoHP), Government of Nepal. In addition, we reviewed several peer-reviewed research papers to summarize the global scenarios and preventive perspectives on COVID-19.Results: In context to Nepal SARS-CoV-2 has spread throughout the country infecting 16,945 persons in all 77 districts, as of 13th July, 2020. Confirmed, cured and death cases experienced an upward trend up to 1st July, 2020 followed by downward trend as of 13th July, 2020. Over 70% of total confirmed cases of reported COVID-19 patients were from the lowland-plain area. Spatial clustering suggested that provinces 2 and 5 were at potentially increased risk of COVID-19, and Province 1 and Bagmati province could be grouped as future "hot spots". In addition, we proposed four strategies namely, identification of the key medical and social elements, discovery and development of treatment options for a future pandemic, investing in ethno-medicine research and epidemic preparedness of health care system to decrease the efficacy of calamities of future pandemics.Conclusion: Our study demonstrates one of the best ways to protect; control and sluggish transmission of SARS-CoV-2 could be achieved by monitoring active ties using GIS spatial analysis. And, the severity of future pandemic could be minimized by integrative action on the abovementioned four different preventive master plans.

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“…Infections of SARS-CoV-2 are actively threatening worldwide general health owing to a shortage of successful antiviral therapies, so in our work antiviral compound prediction (AVC pred) for chosen ligands was used (http://crdd.osdd.net/servers/avcpred/batch.php). In the AVCpred method, Experimental percentage inhibitory from ChEMBL, as a large-scale bioactivity database for drug discovery are used to foresees antiviral compounds against HIV, Hepatitis C virus (HCV), Hepatitis B virus (HBV), Human herpesvirus (HHV), and 26 other viruses (Qureshi et al 2017).…”

Section: Drug-like Characteristicmentioning

confidence: 99%

Investigation of interaction between close and open types of SARS-Cov-2 spike glycoprotein with synthesized and natural Compounds as inhibitor; Molecular Docking Study

Jomhori¹,

Mosaddeghi²

2020

Preprint

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Abstract Purpose Viral diseases are increasingly endangering universal public health because of a shortage of successful antiviral therapies. The novel pandemic 2019 n-Cov2 disease (COVID-19) is recently identified as viral disorder triggered by a new type of coronavirus. This type of coronavirus binds to the host human receptors through the Spike glycoprotein(S) Receptor Binding Domain (RBD). Two types of spike protein have been identified in open and closed states in which the open type causes severe infection. Thus, this receptor is a significant target for antiviral drug design.Methods Totally 111*2 natural and synthetic compounds were chosen from the PubChem database as ligands. To recognize the ability of direct contact between ligands and the binding site of 2019 n-Cov 2 -ACE2 protein, we have docked all compounds to the protein using AutoDock Vina. The FaF3-Drugs, Pan Assay Intrusion Compounds (PAINS), absorption, distribution, metabolism, excretion (ADME) and Lipinski's rules were used to evaluate the drug-like properties of the identified ligands. Antiviral compound prediction (AVC pred) also was used to assess antivirus properties.Results The results showed that seven ligands out of all had interactions with spike protein-angiotensin converting enzyme 2 binding site. We have found that six out of seven ligands show drug-like characteristics. We also found that the fluorophenyl and propane groups of ligands had the best interaction with the binding site of the protein.Conclusion Further, our results showed the ability of these ligands to prevent receptor binding of the spike protein SARS-CoV-2, so they would be considered as novel compounds of COVID-19 therapy drugs.

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AVCpred: an integrated web server for prediction and design of antiviral compounds

Cited by 62 publications

References 64 publications

Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

Anti-Nipah: A QSAR Based Prediction Method to Identify the Inhibitors Against Nipah Virus

Spatial Distribution and Trend Analysis of Current Status of COVID-19 in Nepal and Global Future Preventive Perspectives

Investigation of interaction between close and open types of SARS-Cov-2 spike glycoprotein with synthesized and natural Compounds as inhibitor; Molecular Docking Study

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