论文标题
在生物医学网络上学习的多项任务联合策略用于药物发现
Multi-task Joint Strategies of Self-supervised Representation Learning on Biomedical Networks for Drug Discovery
论文作者
论文摘要
生物医学网络上的自我监督的表示学习（SSL）为药物发现提供了新的机会。但是，如何有效结合多个SSL模型仍然具有挑战性，并且很少探索。因此，我们提出了对药物发现的生物医学网络的自我监督表示学习的多任务联合策略，名为MSSL2Drug。我们设计了六个基本的SSL任务，灵感来自各种模式特征，包括结构，语义和异质生物医学网络中的属性。重要的是，在两个药物发现场景中，通过基于图的多任务对抗学习框架来评估多个任务的15个组合。结果表明了两个重要的发现。 （1）与其他多任务关节模型相比，多模式任务的组合达到了最佳性能。 （2）当具有相同大小的模态时，局部全球组合模型比随机的两任任务组合产生的性能更高。因此，我们猜想多模式和局部全球组合策略可以视为用于药物发现的多任务SSL的指南。
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.
