Swiss Dock 2025: Docking approaches comparison between attracting cavities and autodockvina

Molecular docking is a crucial computational tool in drug discovery enabling the prediction of ligand-target interactions and accelerating the screening of potential drug candidates. This study compares twowidely used docking algorithms AutodockVina and Attracting Cavities to evaluate their efficiency, accuracy and applicability in molecular docking studies. AutodockVina is known for its speed and computational efficiency making it suitable for high-throughput screening. In contrast, Attracting Cavities provides more accurate binding predictions particularly for covalent interactions but it requires significantly more computational time. The studies suggest that AutodockVina is preferable for rapid screening whereas Attracting Cavities is advantageous for detailed validation studies. Despite these advancements, molecular docking faces challenges in flexibility modelling, scoring function accuracy and solvation effects. Future directions involve integrating machine learning and quantum chemistry to improve predictive accuracy. This study highlights the importance of selecting docking algorithms based on study-specific requirements for drug discovery.