Hantavirus mitigation: Synergizing AI, ML, DL and human-in-the-loop for drug discovery and prevention, inspired by COVID-19 pandemic experiences

Abstract

The COVID-19 pandemic highlighted humanity's vulnerability to emerging infectious diseases and the critical need for proactive strategies. While COVID-19's immediate crisis recedes, it's crucial not to underestimate other threats like Hantavirus, which, despite lower transmissibility, carries high fatality rates and lacks specific treatments or vaccines. This paper argues that the lessons and technological advancements from the COVID-19 response offer a vital blueprint for Hantavirus mitigation. We advocate for a rapid, multi-faceted approach, mirroring COVID-19's urgency. This includes implementing personal protective measures (e.g., masks, gloves) proven effective in reducing viral transmission. Crucially, we propose leveraging the significant advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) that matured during the pandemic. These technologies can revolutionize Hantavirus drug discovery by accelerating drug repurposing from existing libraries, executing rapid virtual screening and molecular docking of vast chemical databases to identify inhibitors, and informing de novo drug design. This accelerated process demands a 'Human-in-the-Loop' (HITL) paradigm. While AI/ML/DL offer unparalleled speed in hypothesis generation, human expertise from virologists, clinicians, and public health officials remains indispensable for validating models, interpreting complex biological data, guiding experimental design, and translating insights into effective interventions. By synergizing advanced computational power with invaluable human intuition, we can proactively develop robust therapeutic and preventive strategies for Hantavirus, shifting from reactive crisis management to a data-driven defense against future viral threats.