In current study, three different APAN workflows were proposed for addressing various tasks. Fig. (a) shows the workflow using molecular 2D graph as input to predict ligand specific properties. Its atom pairing layer includes a concatenation block and multiple ABIN blocks. When it is desired to use ligand 3D conformations as input, the workflow in Fig. (b) can be utilized to make predictions on molecular properties. In this case, the atom pairing layer comprises an additional affine transformation block and a Gaussian kernel block to integrate the 3D distance into atom pair embedding. For 2D graph and ligand 3D conformation workflows, the normal Transformer self-attention block is employed in the PPC layer. Fig. (c) shows the workflow for predicting protein-ligand binding affinity, where the axial attention network block is employed in the PPC layer. Given the large dimension of atom pair embedding tensor, a pooling layer was adopted to reduce data dimension, In the pooling layer, two MEAN operations were done on the resulted atom pair embedding to obtain atompair features, where the average operation is performed on all the atom pair vectors.