AI-Driven Drug Repurposing for Viral Hemorrhagic Fevers

AI-driven drug repurposing for viral hemorrhagic fevers (VHFs) refers to the application of artificial intelligence, machine learning, and computational biology to identify existing FDA-approved or clinically-tested pharmaceutical compounds that can be rapidly redeployed as treatments for VHFs such as Ebola, Marburg, Lassa, and Crimean-Congo hemorrhagic fever. This approach dramatically accelerates therapeutic discovery by bypassing early-stage pharmacokinetic and safety trials, leveraging pre-existing clinical data while targeting conserved viral mechanisms1.

The integration of deep learning models, protein-protein interaction networks, and high-throughput screening simulations has positioned AI as a cornerstone in modern pandemic preparedness, particularly for neglected tropical diseases where traditional drug development pipelines face economic and logistical barriers2.

Background & Clinical Context

Viral hemorrhagic fevers are a group of clinically and virologically distinct diseases caused by families of viruses including Filoviridae, Arenaviridae, Bunyaviridae, and Flaviviridae. These pathogens are characterized by high mortality rates, rapid disease progression, and significant public health risk during outbreaks3. Traditional antiviral development for VHFs has historically lagged due to limited commercial incentives, complex viral replication cycles, and safety concerns in human trials4.

Drug repurposing emerged as a pragmatic alternative, shifting focus from de novo compound synthesis to systematic screening of approved therapeutics. Recent advances in AI have transformed this field from hypothesis-driven candidate selection to data-driven, multi-omic target prediction5.

Computational & AI Methodologies

1. Machine Learning for Target Prediction

Supervised and unsupervised ML algorithms analyze large-scale datasets including crystallographic structures, transcriptomic profiles, and host-virus interaction maps. Graph neural networks (GNNs) and transformer-based architectures have proven particularly effective at predicting viral protein conformations and identifying binding pockets compatible with known drug scaffolds6.

2. Multi-Omic Integration

Modern pipelines integrate genomics, proteomics, and metabolomics data to map host pathways hijacked by VHFs. AI models cross-reference these pathways against pharmacological databases (e.g., DrugBank, ChEMBL, CTD) to surface compounds with pleiotropic antiviral potential7.

3. In Silico Screening & Molecular Docking

Deep learning-enhanced molecular docking simulates millions of compound-target interactions, ranking candidates by binding affinity, ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiles, and blood-brain barrier permeability. This reduces experimental validation costs by an estimated 60–75% compared to traditional high-throughput screening8.

Notable Successes & Clinical Applications

• Favipiravir: Originally developed for influenza, AI-guided mechanistic studies revealed broad-spectrum RNA polymerase inhibition, leading to clinical deployment in Ebola and Lassa virus trials9.
• Remdesivir: Though initially designed for Ebola, AI-driven structural optimization and cross-viral affinity modeling accelerated its repurposing for SARS-CoV-2, demonstrating platform adaptability for hemorrhagic and respiratory viruses10.
• Chloroquine/Hydroxychloroquine: Computational screening identified potential disruption of viral entry pathways in Arenaviridae and Filoviridae, though clinical outcomes highlighted the necessity of wet-lab validation alongside AI predictions11.
• Nitazoxanide: AI-based host-targeted repurposing identified immunomodulatory and antiviral synergy against Crimean-Congo hemorrhagic fever, currently undergoing Phase II trials12.

Challenges & Limitations

Despite remarkable progress, AI-driven repurposing faces several hurdles:

• Data Bias: Training datasets overrepresent commercially viable diseases, creating blind spots for neglected VHFs13.
• Biological Complexity: VHFs exploit multiple host pathways; single-target AI predictions often fail to capture polypharmacological requirements14.
• Regulatory Pathways: Repurposed drugs still require rigorous clinical validation, and IP frameworks can delay rapid deployment during emergencies15.
• Model Interpretability: Black-box AI predictions struggle to provide mechanistic transparency required for regulatory approval and clinical trust16.

Future Directions

The next generation of AI repurposing frameworks will likely integrate real-world epidemiological data, federated learning across global health networks, and explainable AI (XAI) architectures. Public-private partnerships are increasingly standardizing open-access viral proteome databases, while CRISPR-based functional genomics will validate AI-predicted host factors with unprecedented precision17.

Furthermore, foundation models trained on multi-modal biomedical literature are beginning to autonomously generate hypotheses, draft trial protocols, and simulate population-level treatment impacts, positioning AI not merely as a screening tool but as a collaborative research agent in pandemic preparedness18.