The invention discloses a method for predicting 
drug-target affinity based on 
protein multi-source 
feature fusion, which comprises the following steps: firstly, constructing a PPI network and an SSN network, extracting 
protein features from the networks, and then collecting 
protein features such as subcellular positions, sequence codes, functional sites and structural domains for protein characterization; and fusing multi-source features by using a variational graph auto-
encoder, and finally, inputting the multi-source features into a full-connection layer in combination with 
drug branches to carry out affinity prediction. According to the invention, a PPI network and an SSN network are constructed, so that biological priori knowledge between a 
target protein and other proteins is learned in addition to focusing on the characteristics of the 
target protein; according to the method, the protein characteristics are extracted and fused from the aspects of protein interaction, sequence similarity and protein subcellular positions for the first time, so that the 
drug-target affinity is predicted, and the prediction accuracy is improved; in addition, the characteristic source of the protein does not comprise a 
protein structure, so that the dependence on the 
protein structure is abandoned.