In current study, we introduced a novel attention method based on atom pair embedding and applied it on scenarios using 2D graph, ligand 3D conformation as well as protein-ligand complex as model input. Comprehensive benchmark studies on two types of prediction tasks, i.e. molecular property and protein-ligand interaction prediction, were carried out. Particularly, a novel axial attention unit was employed for predicting protein-ligand interaction. In general, our models achieved SOTA performance on all the endpoints among compared reference models. We also compared our APAN model using 2D and 3D ligand information. Our results show that using relevant ligand 3D conformation can improve model performance, while using irrelevant ligand 3D conformation in the model doesn't necessarily perform better than 2D based models.