Protein-ligand interactions play a critical role in numerous biological processes and are of great importance in drug discovery and design. Understanding the binding mechanisms between proteins and ligands is crucial for developing effective therapeutics. Experimental techniques, such as X-ray crystallography and Nuclear Magnetic Resonance (NMR) spectroscopy, provide valuable insights into these interactions. However, they can be time-consuming and expensive. In recent years, computational approaches have emerged as powerful tools for studying protein-ligand interactions. These methods leverage the advances in computational biology, molecular modeling and machine learning to predict and analyze protein-ligand binding events in silico. In this article, we will explore some of the key computational approaches used in the study of protein-ligand interactions.
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