Computer-aided drug design and pharmacophore modeling towards the discovery of novel anti-Ebola agents

The Ebola virus belongs to the Filoviridae family of negative-sense RNA viruses and has been responsible for multiple widespread and severe outbreaks of a type of hemorrhagic fever, termed Ebola virus disease, mainly in the African continent. The virus, which is suspected to have a zoonotic origin, constitutes one of the most lethal pathogens, with high transmission rates and lasting effects on survivors. Research on the Ebola virus is made challenging due to a need for strict biosafety measures, while the arsenal of prophylactic and therapeutic measures remains limited. Modern computational methods can accelerate the process of developing candidate therapeutics that target essential proteins for the Ebola virus. In the present study, a pipeline was constructed to employ machine learning, big chemical data and structural bioinformatics towards the generation of novel molecules and the design of pharmacophores targeting the Ebola virus polymerase.