Identification of promising anti-EBOV inhibitors: de novo drug design, molecular docking and molecular dynamics studies.

ABSTRACT: The Ebola virus (EBOV) outbreak was recorded as the largest in history and caused many fatalities. As seen in previous studies, drug repurposing and database filtration were the two major pathways to searching for potent compounds against EBOV. In this study, a deep learning (DL) approach via the LigDream tool was employed to obtain novel and effective anti-EBOV inhibitors. Based on the galidesivir (BCX4430) chemical structure, 100 compounds were collected and inspected using various in silico approaches. Results from the molecular docking study indicated that mol1_069 and mol1_092 were the best two potent compounds with a docking score of -7.1 kcal mol^-1 and -7.0 kcal mol^-1, respectively. Molecular dynamics simulations, in addition to binding energy calculatio

SUBMITTER: Mohamed EAR 

PROVIDER: S-EPMC9515638 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature