微重力生物知识图谱的构建及其在抗骨质疏松症药物预测中的应用

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS ACS Applied Bio Materials Pub Date : 2024-01-29 DOI:10.1016/j.lssr.2024.01.004
Yu-Han Zheng , Guan-Jing Pan , Yuan Quan, Hong-Yu Zhang
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引用次数: 0

摘要

太空环境中的微重力可能会对人体产生各种负面影响,其中之一就是骨质流失。鉴于人类太空活动日益频繁,急需确定微重力环境下有效的抗骨质疏松药物。传统的太空微重力实验存在程序耗时长、成本高、样本量小等局限性。近年来,随着生物信息学和计算机技术的发展,体内药物发现方法已成为一种前景广阔的策略。在本研究中,我们首先收集了184915篇与微重力和骨质流失相关的文献。我们结合使用了依赖路径提取和聚类技术,从文本中提取数据。之后,我们对数据进行了清洗和标准化,整合了多个来源的数据,包括全球生物医学关系网络(GNBR)、药物相互作用数据库(DDInter)、相互作用化学物质搜索工具(STITCH)、药物数据库(DrugBank)和中药综合数据库(TCMID)。通过这一整合过程,我们构建了由 134,796 个生物实体和 3,395,273 个三联体组成的微重力生物学知识图谱(MBKG)。随后,我们利用 TransE 模型执行知识图谱嵌入。通过计算模型空间中实体之间的距离,该模型成功预测了治疗骨质疏松症和微重力诱发骨质流失的潜在药物。结果表明,在排名前 10 位的西药中,有 7 种已被批准用于治疗骨质疏松症。此外,在排名前 10 位的中药中,有 5 种已获得治疗骨质疏松有效性的科学文献支持。在针对微重力诱导骨质流失的前 20 种预测药物中,有 15 种已在微重力或模拟微重力环境中进行了研究,其余 5 种也适用于治疗骨质疏松症。这项研究凸显了 MBKG 在太空药物发现领域的潜在应用。
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Construction of microgravity biological knowledge graph and its applications in anti-osteoporosis drug prediction

Microgravity in the space environment can potentially have various negative effects on the human body, one of which is bone loss. Given the increasing frequency of human space activities, there is an urgent need to identify effective anti-osteoporosis drugs for the microgravity environment. Traditional microgravity experiments conducted in space suffer from limitations such as time-consuming procedures, high costs, and small sample sizes. In recent years, the in-silico drug discovery method has emerged as a promising strategy due to the advancements in bioinformatics and computer technology. In this study, we first collected a total of 184,915 literature articles related to microgravity and bone loss. We employed a combination of dependency path extraction and clustering techniques to extract data from the text. Afterwards, we conducted data cleaning and standardization to integrate data from several sources, including The Global Network of Biomedical Relationships (GNBR), Curated Drug–Drug Interactions Database (DDInter), Search Tool for Interacting Chemicals (STITCH), DrugBank, and Traditional Chinese Medicines Integrated Database (TCMID). Through this integration process, we constructed the Microgravity Biology Knowledge Graph (MBKG) consisting of 134,796 biological entities and 3,395,273 triplets. Subsequently, the TransE model was utilized to perform knowledge graph embedding. By calculating the distances between entities in the model space, the model successfully predicted potential drugs for treating osteoporosis and microgravity-induced bone loss. The results indicate that out of the top 10 ranked western medicines, 7 have been approved for the treatment of osteoporosis. Additionally, among the top 10 ranked traditional Chinese medicines, 5 have scientific literature supporting their effectiveness in treating bone loss. Among the top 20 predicted medicines for microgravity-induced bone loss, 15 have been studied in microgravity or simulated microgravity environments, while the remaining 5 are also applicable for treating osteoporosis. This research highlights the potential application of MBKG in the field of space drug discovery.

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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
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