Fig. 1: The schematic workflow of MSSL2drug.

a, The BioHN is constructed. b,c, Six self-supervised tasks are developed (b), which guide GATs to generate representations from different views in the BioHN (c). e, Representation vectors are generated. d,f, Fifteen kinds of multitask combinations (d) and a graph-attention-based multitask adversarial learning framework (f) are developed. g, The different single- and multitask SSL representations are fed into the MLP. h, The two important findings from MSSL2drug results. All circles, quadrangles and pentagons denote the drugs, proteins and diseases in a BioHN, respectively. The solid lines are the relationships among the biomedical entities in a BioHN. The red nodes represent the randomly selected vertices or node pairs in each of self-supervised task. The red solid lines in the edge type masked prediction (EdgeMask) and bio-path classification (PathClass) modules represent the randomly selected edges or paths, respectively. The red dashed curves in the pairwise distance classification (PairDistance) module represent the measurements of the shortest paths between biomedical entities. The red solid curves in the node similarity regression (SimReg) and node similarity contrast (SimCon) modules represent the measurements of the similarities between biomedical entities. ClusterPre and PairDistance denotes clustering coefficient prediction and a pairwise distance classification, respectively.