Artificial Intelligence‐Powered Molecular Docking

Molecular docking is a vital computational method for predicting how small molecules bind to target proteins, aiding drug discovery. It involves screening vast small molecule databases to identify potential drug candidates, relying on scoring functions to rank them. This interaction between proteins and ligands is the cornerstone of drug design. Integrating artificial intelligence (AI) algorithms has revolutionized this field, boosting efficiency and accuracy. This chapter explores various docking techniques, with a focus on AI-based methods. Neural networks, reinforcement learning, and evolutionary algorithms play pivotal roles, enhancing prediction accuracy and speed by utilizing deep learning models trained on extensive protein– ligand datasets. This integration has the potential to expedite drug discovery. Recognizing that AI is not a standalone solution, the chapter emphasizes the need for integration with other methodologies to achieve comprehensive drug discovery. It also addresses the challenges and limitations of AI in molecular docking, pointing toward future research directions. In summary, AI-driven advancements in molecular docking offer a promising pathway to accelerate drug discovery while recognizing the need for a holistic approach in the field.