Anti-Ebola: an initiative to predict Ebola virus inhibitors through machine learning.

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,

SUBMITTER: Rajput A 

PROVIDER: S-EPMC8343361 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature