Advancements and novel approaches in modified AutoDock Vina algorithms for enhanced molecular docking

Abstract

Molecular docking plays a crucial role in modern drug discovery by facilitating the prediction of interactions between small molecules and biomolecular targets. AutoDock Vina (Vina) has earned its reputation as a leading software thanks to its effective energy-based scoring system and user-friendly interface. However, the growing demands of computational biology have prompted investigations into improvements for Vina, leading to a range of algorithmic enhancements. This systematic review explores the recent developments achieved by Vina for molecular docking. These modifications include hybrid parallelization methods utilizing high-performance computing and innovative scoring functions integrated with machine learning. The review examines the difficulties and possibilities associated with these adapted algorithms, shedding light on their diverse origins and potential collaboration across computational chemistry, machine learning, structural biology, and emerging technologies.

Keywords

• AutoDock Vina
• Molecular docking
• Computational chemistry
• Machine learning
• High-performance computing