Ligand Pose

Ligand pose prediction, crucial for drug discovery, aims to determine the optimal three-dimensional orientation of a small molecule (ligand) when bound to a protein. Current research heavily utilizes deep learning, employing architectures like equivariant transformers and diffusion models, to improve the accuracy and efficiency of pose prediction, often focusing on enhanced sampling techniques to overcome limitations of traditional methods. These advancements are improving the ability to predict protein-ligand interactions, leading to more accurate virtual screening and ultimately accelerating drug development. The development of new datasets and open-source tools further facilitates progress in this critical area.