The article compares quantum-mechanical protein-ligand scoring functions with traditional single-structure methods, focusing on their effectiveness in predicting binding affinities. Quantum-mechanical approaches harness computational advancements, offering precise electronic-level insights, which enhances the accuracy of binding predictions. In contrast, standard methods often rely on static representations, potentially missing dynamic interactions. To address this, molecular dynamics (MD)-based free energy calculations were employed. These calculations simulate protein-ligand interactions over time, providing a dynamic perspective. The study reveals that while conventional scoring functions are computationally efficient, integrating quantum mechanics and MD data significantly improves accuracy, paving the way for more reliable drug discovery processes.
