Evotec (UK) Ltd., Abingdon, Oxfordshire, UK.
Evotec SE, Hamburg, Germany.
Methods Mol Biol. 2024;2716:203-221. doi: 10.1007/978-1-0716-3449-3_9.
Knowledge graphs represent information in the form of entities and relationships between those entities. Such a representation has multiple potential applications in drug discovery, including democratizing access to biomedical data, contextualizing or visualizing that data, and generating novel insights through the application of machine learning approaches. Knowledge graphs put data into context and therefore offer the opportunity to generate explainable predictions, which is a key topic in contemporary artificial intelligence. In this chapter, we outline some of the factors that need to be considered when constructing biomedical knowledge graphs, examine recent advances in mining such systems to gain insights for drug discovery, and identify potential future areas for further development.
知识图谱以实体及其之间关系的形式表示信息。这种表示形式在药物发现中有多种潜在的应用，包括将生物医学数据民主化、将该数据语境化或可视化，以及通过应用机器学习方法生成新的见解。知识图谱将数据置于上下文中，因此提供了生成可解释预测的机会，这是当代人工智能的一个关键主题。在本章中，我们概述了构建生物医学知识图谱时需要考虑的一些因素，研究了最近在挖掘这些系统以获取药物发现见解方面的进展，并确定了进一步发展的潜在未来领域。
