MOLECULAR DOCKING: PRINSIP, APLIKASI, TANTANGAN, DAN ARAH PENGEMBANGAN

Molecular docking is a foundational computational approach that predicts how small molecules bind to target biomolecules, enabling the identification of binding modes and affinities crucial for structure-based drug discovery and other molecular research. It employs algorithms and scoring functions to sample conformations and estimate interaction energies, evolving from rigid docking to methods that account for protein flexibility and induced-fit mechanisms. Applications span virtual screening, lead optimization, drug repurposing, and mechanistic studies, aiding efficient candidate prioritization while reducing experimental cost and time. However, challenges such as handling receptor flexibility, scoring function limitations, and dependence on structural data quality persist, motivating hybrid and AI-enhanced strategies. Future directions emphasize integration with machine learning, improved scoring schemes, and quantum-inspired approaches to enhance predictive power and broaden applicability across drug discovery and molecular modeling domains.