The other scenario is to take protein-ligand complex structures as input to predict the protein-ligand binding affinity. Here, the core idea is to learn representations of key protein-ligand atom pairs for predicting protein-ligand interaction. Since a protein has large number of atoms, only atoms close to binding site are considered. In order to describe the protein-ligand binding site, we calculate the Euclidean distance between pocket atoms and ligand atoms. To reduce the computation cost, the pocket atoms are defined as protein atoms whose distance to ligand atoms d cutoff is within 3.5 Å. After preparing a set of pocket atoms, for each protein-ligand atom pair, as shown in Figure , a similar pairing process (from Equation 11-14) is employed to form initial atom pair embedding. The main difference to the formal scenario is that E i and E j are the atomic features of the pocket-ligand atom pair (i, j), instead of intra-molecular atoms. Another difference is that the ABIN blocks in the atom pairing layer are skipped, since protein pocket is not a single molecular entity. Once initial atom pair embeddings are obtained, they are passed into the PPC layer for making self-attention. One type of PPC module is built on the conventional Transformer encoder framework , where the self-attention unit is the main building block. For ligand specific tasks, the atom pair embedding tensor is symmetric and the normal self-attention block is used (as shown in Fig. ). For protein-ligand system, to reduce computation cost, we proposed a novel way to carry out self-attention. The atom pairs are arranged in a non-symmetric way, i.e. ligand atoms are listed in rowwise and pocket atoms are arranged in column-wise, as shown in Fig. (Left). To process such a non-symmetric information, recently proposed axial attention network is employed to learn the representation of ligand/protein 3D complex structures.