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.
翻译:在生物医学网络上自我监督的代表性学习(SSL)为药物发现提供了新的机会,然而,如何有效地将多种SSL模型结合起来仍然具有挑战性,而且很少加以探讨。因此,我们提出了在药物发现生物医学网络上自我监督的代表性学习(称为MSSL2drug)的多任务联合战略。我们设计了六种基本SSL任务,这些任务受多种模式特征的启发,包括结构、语义和生物医学多样性网络的属性。重要的是,在两种药物发现情景中,15种多重任务组合由基于注意的多任务对抗性学习框架来评估。结果显示两个重要结论:(1) 多式联运任务组合与其他多任务联合模型相比,取得最佳业绩。(2) 当模式大小相同时,当地-全球组合模式的性能高于随机的两种任务组合。因此,我们推测,多式联运和本地-全球组合战略可以被视为多种任务SLSL药物发现的指导方针。