A deep-learning system bridging molecule structure and biomedical text with comprehension comparable to human professionals

• Published in: Nature communications Vol. 13; no. 1; pp. 862 - 11
• Main Authors: Zeng, Zheni, Yao, Yuan, Liu, Zhiyuan, Sun, Maosong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.02.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/1305; 631/154; Algorithms; Biomedical data; Biomedical materials; Biomedical Research; Bridges; Data Mining; Deep Learning; Drug Discovery - methods; Electronic Data Processing; Humanities and Social Sciences; Humans; Information sources; Information Storage and Retrieval; Learning; Medical research; Molecular Structure; multidisciplinary; Natural Language Processing; Reading; Science; Science (multidisciplinary)
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-28494-3

To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from versatile reading on both molecule structure and biomedical text information, we propose a knowledgeable machine reading system that bridges both types of information in a unified deep-learning framework for comprehensive biomedical research assistance. We solve the problem that existing machine reading models can only process different types of data separately, and thus achieve a comprehensive and thorough understanding of molecule entities. By grasping meta-knowledge in an unsupervised fashion within and across different information sources, our system can facilitate various real-world biomedical applications, including molecular property prediction, biomedical relation extraction and so on. Experimental results show that our system even surpasses human professionals in the capability of molecular property comprehension, and also reveal its promising potential in facilitating automatic drug discovery and documentation in the future. To accelerate biomedical research process, deep-learning systems are developed to automatically acquire knowledge about molecule entities by reading large-scale biomedical data. Inspired by humans that learn deep molecule knowledge from both molecule structure and biomedical text information, the authors propose a machine reading system that bridges both types of information.