Molecular docking is a valuable computational technique in modern drug discovery and molecular biology, serving as a cornerstone for predicting interactions between small molecules and biological macromolecules. Recent advancements in computational methodologies have transformed molecular docking from a simple lock-and-key concept to sophisticated algorithms incorporating protein flexibility and induced-fit mechanisms. The fundamental principles of molecular docking encompass multiple theoretical frameworks, including conformational selection and ensemble approaches, all governed by thermodynamic principles seeking lowest Gibbs free energy states. Current docking protocols employ varied sampling algorithms and scoring functions, ranging from force-field based to knowledge-based statistical approaches. The evolution of docking software has led to diverse tools, each with specific strengths in handling different types of molecular interactions. Applications span across pharmaceutical research, from hit identification and lead optimization to understanding protein-protein interactions and enzymatic mechanisms. Integration with artificial intelligence and machine learning has further enhanced docking accuracy and efficiency. The methodological sophistication in molecular docking continues to advance, offering improved precision in predicting binding modes and affinities, while overcoming challenges in protein flexibility and scoring function accuracy. These developments makes molecular docking as an indispensable tool in drug design, structural biology, and environmental science applications.
Anusha et al. (Tue,) studied this question.