Integrative computational approaches in the quest for novel Ebolavirus therapeutics

Ebolavirus (EBOV) is a highly pathogenic member of the Filoviridae family, causing severe hemorrhagic fever with high mortality rates and is persistent threat to global health with recurrent outbreaks. Although advances, including vaccine and monoclonal antibody therapeutic development, have improved survival chances, challenges such as cost and availability remain as well as potential viral evolution. Consequently, there is an urgent need for alternative, cost-effective and scalable therapeutics, which has driven interest in computational drug discovery as rapid response strategies.

This review synthesizes structural and functional insights into EBOV's key molecular targets and explores their roles in viral entry, replication, and immune evasion. The authors also discuss host-pathogen interactions as potential therapeutic routes of entry and present the most recent computational advances for hit identification and optimization. There is also coverage given to case studies highlighting successful in silico discovery efforts. Literature identified via PubMed, Scopus, and Web of Science were utilized to compose this manuscript with the authors focusing on on recently published studies, while also utilizing their own experience and insight.

While promising, translation of Ebola viral research remains limited by incomplete structural coverage, small and imbalanced datasets, and a gap between computational prediction and experimental validation. Hybrid in silico workflows are embedded within standardized, data-driven, and experimentally anchored pipelines, and are essential to bridge this gap. By uniting computational precision with laboratory validation, integrative strategies can accelerate the development of robust, deployable EBOV therapeutics and serve as a model for pandemic preparedness against other high-risk pathogens.