The framework of the proposed GVDTI model, take |${r_{1}}$| and |${p_{2}}$| as examples. The topological embedding vector of each drug or protein node is learned by a graph convolutional autoencoder (a) and (b), and the multi-layer convolutional neural network is used to fuse the topology embedding of |${r_{1}}$| - |${p_{2}}$|(c). (d) A tri-layer heterogeneous network is constructed, and the proposed embedding strategy is used to form an attribute-embedding matrix of |${r_{1}}$| - |${p_{2}}$|⁠. (e) Pairwise attribute distribution representation is extracted using a convolutional variational autoencoder. (f) Pairwise attribute representation is obtained by multi-layer convolutional coding. (g) Fusion of the three pairwise representations, attention-enhanced topological representation, attribute distribution and attribute representation.