  Predicting the docking between proteins and ligands is a crucial and
challenging task for drug discovery. However, traditional docking methods
mainly rely on scoring functions, and deep learning-based docking approaches
usually neglect the 3D spatial information of proteins and ligands, as well as
the graph-level features of ligands, which limits their performance. To address
these limitations, we propose an equivariant transformer neural network for
protein-ligand docking pose prediction. Our approach involves the fusion of
ligand graph-level features by feature processing, followed by the learning of
ligand and protein representations using our proposed TAMformer module.
Additionally, we employ an iterative optimization approach based on the
predicted distance matrix to generate refined ligand poses. The experimental
results on real datasets show that our model can achieve state-of-the-art
performance.
