Deep-Learning vs Physics-Based Docking Tools for Future Coronavirus Pandemics

The COVID-19 pandemic revealed the urgent need for faster and more accessible drug discovery for future coronavirus outbreaks. We participated in the Polaris Antiviral Drug Discovery 2025 competition to evaluate how AI can accelerate this process, comparing three molecular docking approaches: AutoDock Vina (physics-based), GNINA (deep learning-assisted), and Boltz-2 (data-driven), using SARS-CoV-2 and MERS-CoV data sets. Our results demonstrate that AI has transformed drug discovery: Boltz-2 reached over 80% accuracy, greatly improving docking <i>precision</i>. However, accurate prediction of binding <i>potency</i> remains challenging and requires further methodological advances. Traditional methods like Vina and GNINA are faster, making them suitable for large-scale screening, and their physical basis is easier to interpret, with room for future improvement. By combining modeling and analysis tools into easy-to-use platforms like ChemOrchestra, drug discovery can become more <i>decentralized</i> and accessible, allowing people to use advanced models quickly.