AI-driven drug repurposing and binding pose meta dynamics identifies novel targets for monkeypox virus

Area of Science:

• Virology
• Computational Biology
• Drug Discovery

Background:

• Monkeypox virus (MPXV) emerged as a global health emergency in 2022.
• MPXV virions possess a unique structure, including a double-stranded DNA genome and viral-host protein interactions, presenting a potential therapeutic target.

Purpose of the Study:

• To identify existing drugs that can inhibit MPXV viral proteins using an AI-driven approach.
• To leverage computational methods for rigorous filtering and validation of potential drug candidates.

Main Methods:

• Utilized DeepRepurpose, an AI framework, in a transfer learning setting to predict drug-viral protein interactions.
• Employed a computational pipeline including homology modeling, molecular docking, dynamic simulations, binding free energy calculations, and metadynamics.

Main Results:

• Prioritized a set of FDA-approved and investigational drugs with potential MPXV inhibitory activity.
• Identified Elvitegravir as a lead compound with significant potential to inhibit MPXV viral proteins through the comprehensive computational analysis.

Conclusions:

• Elvitegravir is a promising candidate for further investigation as a therapeutic agent against Monkeypox virus.
• The AI-based and computational framework provides an efficient strategy for repurposing drugs against emerging viral threats.