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.

应用等变性转换神经网络模型，利用蛋白质和配体的 3D 空间信息以及配体的图级特征进行融合和学习，实现了蛋白质 - 配体对接位姿的预测，并通过迭代优化生成精炼的配体位姿，实验证明该模型具有最先进的性能。