Abstract
Protein-ligand interactions play a crucial role in drug discovery, as they are the basis for designing therapeutic agents. Computational tools and techniques for predicting protein-ligand interactions have become indispensable in modern drug discovery pipelines, offering insights into binding affinity, specificity, and potential drug efficacy. This article discusses the various computational methods used to predict protein-ligand interactions, including molecular docking, molecular dynamics simulations, and machine learning approaches. We also explore the challenges, limitations, and future directions in this field, including the integration of multi-omics data and the application of artificial intelligence for improved predictions.
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