Molecular Docking

Computational chemistry, particularly molecular docking, has become an essential approach in modern drug discovery and molecular design. Docking simulations enable the prediction of ligand protein interactions, binding affinities, and conformational orientations at the molecular level, supporting the identification of potent bioactive compounds prior to synthesis. The integration of computational techniques such as QSAR (Quantitative Structure–Activity Relationship), molecular dynamics (MD), and ADMET modeling enhances predictive accuracy and provides a deeper understanding of molecular mechanisms. Furthermore, the incorporation of Artificial Intelligence (AI), Machine Learning (ML), and Quantum Mechanics (QM) has revolutionized docking precision and data-driven drug design. Despite limitations related to receptor flexibility, solvent effects, and scoring function reliability, validation through redocking, MM-PBSA, and MD simulations ensures trustworthy outcomes. Overall, molecular docking serves as a cost-effective, efficient, and reliable framework for rational compound design, bridging theoretical modeling and experimental validation in modern pharmaceutical and biochemical research.