College of Computer Science and Technology, Dalian University of Technology, Hongling Road, Dalian, 116023, China.
Beijing Institute of Health Administration and Medical Information, Beijing, 100850, China.
BMC Bioinformatics. 2018 May 30;19(1):193. doi: 10.1186/s12859-018-2167-5.
Drug discovery is the process through which potential new medicines are identified. High-throughput screening and computer-aided drug discovery/design are the two main drug discovery methods for now, which have successfully discovered a series of drugs. However, development of new drugs is still an extremely time-consuming and expensive process. Biomedical literature contains important clues for the identification of potential treatments. It could support experts in biomedicine on their way towards new discoveries.
Here, we propose a biomedical knowledge graph-based drug discovery method called SemaTyP, which discovers candidate drugs for diseases by mining published biomedical literature. We first construct a biomedical knowledge graph with the relations extracted from biomedical abstracts, then a logistic regression model is trained by learning the semantic types of paths of known drug therapies' existing in the biomedical knowledge graph, finally the learned model is used to discover drug therapies for new diseases.
The experimental results show that our method could not only effectively discover new drug therapies for new diseases, but also could provide the potential mechanism of action of the candidate drugs.
In this paper we propose a novel knowledge graph based literature mining method for drug discovery. It could be a supplementary method for current drug discovery methods.
药物发现是指识别潜在新药的过程。高通量筛选和计算机辅助药物发现/设计是目前两种主要的药物发现方法，它们已经成功发现了一系列药物。然而，开发新药仍然是一个极其耗时和昂贵的过程。生物医学文献包含了识别潜在治疗方法的重要线索。它可以为生物医学领域的专家提供新发现的支持。
在这里，我们提出了一种基于生物医学知识图的药物发现方法，称为 SemaTyP，通过挖掘已发表的生物医学文献来发现针对疾病的候选药物。我们首先从生物医学摘要中提取关系来构建生物医学知识图，然后通过学习生物医学知识图中已知药物治疗方法的路径的语义类型来训练逻辑回归模型，最后使用学习到的模型来发现针对新疾病的药物治疗方法。
实验结果表明，我们的方法不仅可以有效地为新疾病发现新的药物治疗方法，还可以提供候选药物的潜在作用机制。
本文提出了一种基于知识图的文献挖掘药物发现新方法。它可以作为当前药物发现方法的补充方法。
