The integration of machine learning (ML) and artificial intelligence (AI) into molecular docking represents a significant advancement in the field of drug discovery and computational chemistry. These technologies are being used to enhance the accuracy, efficiency, and predictive power of molecular docking simulations. One of the primary applications of ML in molecular docking is the development of improved scoring functions. Traditional scoring functions often struggle to accurately predict binding affinities due to their reliance on simplified physical models. ML models, trained on large datasets of known ligand-receptor interactions, can learn complex patterns and interactions, leading to more accurate predictions of binding affinities. ML algorithms can significantly speed up the virtual screening process by learning to predict which molecules are likely to bind to a given target. This can be particularly useful in high-throughput screening, where ML models can prioritize potentially active compounds for further investigation, thereby reducing the need for extensive experimental assays. AI techniques, particularly deep learning models, can be trained to predict the likely binding poses of ligands within a receptor's active site. These models can learn from large datasets of known ligand-receptor complexes, capturing subtle patterns that may not be apparent through traditional docking methods. AI models can also be used to address the challenge of protein flexibility in docking simulations. By training on dynamic data from molecular dynamics simulations, ML models can predict how protein conformational changes affect ligand binding, which is a significant challenge for traditional docking methods that often consider the protein as a rigid body .