An update on knowledge graphs and their current and potential applications in drug discovery.

IF 6 2区 医学 Q1 PHARMACOLOGY & PHARMACY
Expert Opinion on Drug Discovery Pub Date : 2025-05-01 Epub Date: 2025-04-14 DOI:10.1080/17460441.2025.2490253
Angela Serra, Michele Fratello, Antonio Federico, Dario Greco
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引用次数: 0

Abstract

Introduction: Knowledge graphs are becoming prominent tools in computational drug discovery. They effectively integrate heterogeneous biomedical data and generate new hypotheses and knowledge.

Areas covered: This article is based on a literature review using Google Scholar and PubMed to retrieve articles on existing knowledge graphs relevant to the drug discovery field. The authors compare the types of entities, relationships, and data sources they encompass. Additionally, the authors provide examples of their use in the drug discovery field and discuss potential strategies for advancing this research area.

Expert opinion: Knowledge graphs are crucial in drug discovery, but their construction leads to challenges in data integration and consistency. Future research should prioritize the standardization of data sources and data modeling. More efforts are needed for the integration in knowledge graphs of diverse data types, such as chemical structures and epigenetic data, to enhance their effectiveness. Additionally, advancements in large language models should be pursued to aid the development of knowledge graphs, provide intuitive querying capabilities for non-expert users, and explain knowledge graphs -derived predictions, thereby making these tools more accessible and their insights more interpretable for a wider audience.

知识图谱的最新进展及其在药物发现中的当前和潜在应用。
知识图谱正在成为计算药物发现的重要工具。他们有效地整合异质生物医学数据,产生新的假设和知识。涉及领域:本文基于使用b谷歌Scholar和PubMed检索与药物发现领域相关的现有知识图谱上的文章的文献综述。作者比较了它们所包含的实体、关系和数据源的类型。此外,作者还提供了它们在药物发现领域的应用实例,并讨论了推进这一研究领域的潜在策略。专家意见:知识图谱在药物发现中至关重要,但其构建导致数据集成和一致性方面的挑战。未来的研究应优先考虑数据源的标准化和数据建模。不同数据类型的知识图谱,如化学结构和表观遗传数据,需要更多的努力来整合,以提高其有效性。此外,应该追求大型语言模型的进步,以帮助知识图的发展,为非专业用户提供直观的查询功能,并解释知识图派生的预测,从而使这些工具更容易访问,并且它们的见解对更广泛的受众更易于解释。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
10.20
自引率
1.60%
发文量
78
审稿时长
6-12 weeks
期刊介绍: Expert Opinion on Drug Discovery (ISSN 1746-0441 [print], 1746-045X [electronic]) is a MEDLINE-indexed, peer-reviewed, international journal publishing review articles on novel technologies involved in the drug discovery process, leading to new leads and reduced attrition rates. Each article is structured to incorporate the author’s own expert opinion on the scope for future development. The Editors welcome: Reviews covering chemoinformatics; bioinformatics; assay development; novel screening technologies; in vitro/in vivo models; structure-based drug design; systems biology Drug Case Histories examining the steps involved in the preclinical and clinical development of a particular drug The audience consists of scientists and managers in the healthcare and pharmaceutical industry, academic pharmaceutical scientists and other closely related professionals looking to enhance the success of their drug candidates through optimisation at the preclinical level.
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