  Self-supervised representation learning (SSL) on biomedical networks provides
new opportunities for drug discovery. However, how to effectively combine
multiple SSL models is still challenging and has been rarely explored.
Therefore, we propose multi-task joint strategies of self-supervised
representation learning on biomedical networks for drug discovery, named
MSSL2drug. We design six basic SSL tasks inspired by various modality features
including structures, semantics, and attributes in heterogeneous biomedical
networks. Importantly, fifteen combinations of multiple tasks are evaluated by
a graph attention-based multi-task adversarial learning framework in two drug
discovery scenarios. The results suggest two important findings. (1)
Combinations of multimodal tasks achieve the best performance compared to other
multi-task joint models. (2) The local-global combination models yield higher
performance than random two-task combinations when there are the same size of
modalities. Therefore, we conjecture that the multimodal and local-global
combination strategies can be treated as the guideline of multi-task SSL for
drug discovery.
