@article{open2725,
           month = {August},
           title = {Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning},
          author = {Akanksha Rajput and Manoj Kumar},
       publisher = {Springer },
            year = {2021},
            note = {Copyright of this article belongs to SPRINGER},
         journal = {MOLECULAR DIVERSITY},
        keywords = {Ebola virus; Machine learning; Prediction algorithm; QSAR; Random forest; Web server.},
             url = {http://crdd.osdd.net/open/2725/},
        abstract = {Ebola virus is a deadly pathogen responsible for a frequent series of outbreaks since 1976. Despite various efforts from researchers worldwide, its mortality and fatality are quite high. For antiviral drug discovery, the computational efforts are considered highly useful. Therefore, we have developed an 'anti-Ebola' web server, through quantitative structure-activity relationship information of available molecules with experimental anti-Ebola activities. Three hundred and five unique anti-Ebola compounds with their respective IC50 values were extracted from the 'DrugRepV' database. Later, the compounds were used to extract the molecular descriptors, which were subjected to regression-based model development. The robust machine learning techniques, namely support vector machine, random forest and artificial neural network, were employed using tenfold cross-validation. After a randomization approach, the best predictive model showed Pearson's correlation coefficient ranges from 0.83 to 0.98 on training/testing (T274) dataset. The robustness of the developed models was cross-evaluated using William's plot. The highly robust computational models are integrated into the web server. The 'anti-Ebola' web server is freely available at https://bioinfo.imtech.res.in/manojk/antiebola . We anticipate this will serve the scientific community for developing effective inhibitors against the Ebola virus.}
}