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arXiv 2022年

作者： Wang, Xiaoqi Cheng, Yingjie Yang, Yaning Yu, Yue Li, Fei Peng, Shaoliang College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Institute of Open Source Software and Platform Peng Cheng Laboratory Shenzhen518000 China Computer Network Information Center Chinese Academy of Sciences Beijing100850 China The State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan University Changsha410082 China

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 multitask 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. © 2022, CC BY-SA.

关键词： Semantics

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