įurthermore, various computational approaches have been tried to identify repurposed or novel leads against EBOV. Recently, we have developed a comprehensive repository of experimentally validated repurposed drugs against 23 viruses (including Ebola virus) responsible for causing epidemics/pandemics. However, our group previously implemented the machine learning approaches to develop computational methods to predict the antiviral compounds against various viruses like flaviviruses, Nipah virus and coronaviruses as AVCpred, anti-Flavi and anti-Nipah and anti-corona, respectively. design the Ser/Thr-protein kinase inhibitors by using machine-trained elastic networks. Matta CF explored the role of biophysical and biological properties in the formulation of QSAR models. explained the importance of physicochemical parameters in the quantitative structure–activity relationship (QSAR) analysis. described the importance of molecular descriptors in the process of designing the efficient drugs. Numerous computational studies are reported in the literature to highlight the use of machine learning in drug development against various pathogens. Likewise, anti-Ebola drug Remdesivir was also repurposed to inhibit murine hepatic virus (MHV), Middle East respiratory syndrome (MERS-CoV), severe acute respiratory syndrome (SARS-CoV) and Nipah virus (NiV). Initially, the Favipiravir was used to treat influenza virus, but now has been used against EBOV. The Favipiravir (6-fluoro-3-hydroxy-2-pyrazinecarboxamide) and Remdesivir (GS-5734) are in use as the broad-spectrum antiviral drugs. It is the first USFDA-approved therapeutics in 2020 against EBOV infection. INMAZEB, also known as REGN-EB3, is a mixture of three monoclonal antibodies, namely atoltivimab, maftivimab and odesivimab. Among them, Favipiravir and Remdesivir are the ‘experimental’ category drugs that inhibit the viral polymerases while the ZMapp is the mixture of the three monoclonal antibodies, which are directed against the surface glycoproteins. Currently, Favipiravir, Remdesivir, ZMapp and INMAZEB are the four most commonly used anti-Ebola agents for the treatment of EBOV infection. As EBOV is an RNA virus, thus the development of effective antivirals against EBOV is a very challenging task. The structural proteins include the nucleoprotein (NP), glycoprotein (GP), soluble glycoprotein (sGP), RNA-dependent RNA polymerase (L) and four virion proteins (VP24, VP30, VP35, VP40). It constitutes eight structural and one nonstructural proteins. As per WHO, the EBOV outbreak is classified under level 3 emergency due to its high mortality and fatality.ĮBOV is a negative-stranded, enveloped, non-segmented and helical single-stranded RNA with 19-kb nucleotides. EBOV cases are mainly found in the region of sub-Saharan Africa and pass-through animals like a bat, other nonhuman primates or any patient infected with EBOV. According to the World Health Organization (WHO), the fatality rate of the EBOV outbreak varies from 25 to 90% ( ). EBOV is responsible for thousands of deaths due to its periodic outbreaks since 1976. Graphic abstractĮbola virus (EBOV) is a member of Filoviridae family also known as Zaire ebolavirus, on the basis of the origin country, i.e., Democratic Republic of Congo (formerly Zaire). We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus. The ‘anti-Ebola’ web server is freely available at. The highly robust computational models are integrated into the web server. The robustness of the developed models was cross-evaluated using William’s plot. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T 274) dataset. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. Three hundred and five unique anti-Ebola compounds with their respective IC 50 values were extracted from the ‘DrugRepV’ database. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure–activity relationship information of available molecules with experimental anti-Ebola activities. For antiviral drug discovery, the computational efforts are considered highly useful. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976.
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