Nature：一种使用知识图自动生成药物发现生物证据的实验验证方法-动脉网

Nature：An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs

Nature 等信源发布 2024-07-08 15:53

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AbstractExplaining predictions for drug repositioning with biological knowledge graphs is a challenging problem. Graph completion methods using symbolic reasoning predict drug treatments and associated rules to generate evidence representing the therapeutic basis of the drug. Yet the vast amounts of generated paths that are biologically irrelevant or not mechanistically meaningful within the context of disease biology can limit utility.

摘要用生物学知识图解释药物重新定位的预测是一个具有挑战性的问题。使用符号推理的图形完成方法预测药物治疗和相关规则，以生成代表药物治疗基础的证据。然而，在疾病生物学的背景下，大量产生的与生物学无关或没有机械意义的路径可能会限制效用。

We use a reinforcement learning based knowledge graph completion model combined with an automatic filtering approach that produces the most relevant rules and biological paths explaining the predicted drug’s therapeutic connection to the disease. In this work we validate the approach against preclinical experimental data for Fragile X syndrome demonstrating strong correlation between automatically extracted paths and experimentally derived transcriptional changes of selected genes and pathways of drug predictions Sulindac and Ibudilast.

我们使用基于强化学习的知识图完成模型，结合自动过滤方法，产生最相关的规则和生物路径，解释预测药物与疾病的治疗联系。在这项工作中，我们验证了针对脆性X综合征的临床前实验数据的方法，证明了自动提取的途径与所选基因的实验衍生转录变化以及药物预测途径舒林酸和依布地司特之间的强相关性。

Additionally, we show it reduces the number of generated paths in two case studies, 85% for Cystic fibrosis and 95% for Parkinson’s disease..

此外，我们在两个案例研究中显示，它减少了生成路径的数量，囊性纤维化为85%，帕金森病为95%。。

IntroductionDiscovering safe and effective treatments for rare diseases presents a formidable challenge, starting with the sourcing, normalising, and integration of copious, diffuse and diverse data sources that inform drug discovery. When it comes to the more than 7000 rare and genetic disorders, valuable information is frequently dispersed across various databases, encompassing clinical symptoms, impacted pathways, animal models, and potential treatments.

引言发现罕见疾病的安全有效治疗方法是一项艰巨的挑战，首先是寻找，规范和整合丰富，分散和多样化的数据源，为药物发现提供信息。当涉及7000多种罕见和遗传疾病时，有价值的信息经常分散在各种数据库中，包括临床症状，受影响的途径，动物模型和潜在的治疗方法。

To address this issue, AI-driven computational tools and knowledge can be harnessed to interconnect this diverse data, enabling the prediction of innovative drug candidates. Typically, existing computational methods generate an overwhelming number of therapeutic hypotheses, necessitating labour-intensive manual curation by experts specializing in the respective disease.

。通常，现有的计算方法会产生大量的治疗假设，因此需要专门研究各种疾病的专家进行劳动密集型的手动治疗。

This process involves a significant amount of time dedicated to establishing the therapeutic linkage between the drug and the disease, given the identification of the mechanism of action is pivotal in establishing clinical tractability.Knowledge graphs (KG) have been used extensively in the recent past to solve complex problems in life sciences including drug discovery for rare diseases.

鉴于确定作用机制对于建立临床易处理性至关重要，因此该过程需要花费大量时间来建立药物与疾病之间的治疗联系。最近，知识图（KG）已被广泛用于解决生命科学中的复杂问题，包括罕见疾病的药物发现。

Knowledge graphs are constructed with head entity-relation-tail entity (h, r, t) triples where entities correspond to nodes and relations correspond to links connecting the entities. Biological knowledge graphs are constructed with biological nodes such as drugs, diseases, genes, pathways, phenotypes, proteins etc and the links between these nodes.

知识图由头-实体-关系-尾-实体（h，r，t）三元组构成，其中实体对应于节点，关系对应于连接实体的链接。生物知识图由药物、疾病、基因、途径、表型、蛋白质等生物节点以及这些节点之间的链接构成。

Knowledge base completion (KBC)1 is the task of predicting the tail entity t, given the head entity h, and the relation r, or head entity h given the tail entity t and relation r. It can also be used to predict unseen relat.

知识库完成（KBC）1的任务是预测尾部实体t，给定头部实体h，以及关系r，或给定尾部实体t和关系r的头部实体h。它也可以用于预测不可见的关系。

(1)

$${{{{{\rm{Compound}}}}}}{-}treats \rightarrow {{{{{\rm{Disease}}}}}}{-}ancestor \rightarrow {{{{{\rm{Disease}}}}}}$$

$${{{{\rm{化合物}}}}{-}treats{-}ancestor\右箭头{{{{\rm{疾病}}}}}$$

(2)

$${{{{{\rm{Compound}}}}}}{-}binds \rightarrow {{{{{\rm{Gene}}}}}}{-}participates \rightarrow {{{{{\rm{pathway}}}}}}{-}involves \rightarrow {{{{{\rm{Disease}}}}}}$$

$${{{{\rm{化合物}}}}{-}binds\右箭头{{{{\rm{基因}}}}}{-}participates\右箭头{{{{\rm{路径}}}}}{-}involves\右箭头{{{{\rm{疾病}}}}}$$

(3)

$${{{{{\rm{Compound}}}}}}{-}involves \rightarrow {{{{{\rm{Pathway}}}}}}{-}involves \rightarrow {{{{{\rm{Disease}}}}}}{-}ancestor \rightarrow {{{{{\rm{Disease}}}}}}$$

$${{{{\rm{化合物}}}}{-}involves\右箭头{{{{\rm{路径}}}}}{-}involves\右箭头{{{{\rm{疾病}}}}}{-}ancestor\右箭头{{{{\rm{疾病}}}}}$$

(4)

$$ {{{{{\rm{Compound}}}}}}{-}treats \rightarrow {{{{{\rm{Disease}}}}}}{-}associates \rightarrow {{{{{\rm{Gene}}}}}} \\ \leftarrow associates{-}{{{{{\rm{Disease}}}}}}{-}ancestor \rightarrow {{{{{\rm{Disease}}}}}}$$

$${{{{\rm{化合物}}}}{-}treats\右箭头{{{{\rm{疾病}}}}}{-}associates\ rightarrow{{{\rm{Gene}}}}}\\ leftarrow关联{-}{{{\rm{Disease}}}}{-}ancestor\右箭头{{{{\rm{疾病}}}}}$$

(5)

For example, rules (1) and (2) shown above frequently receive high confidence scores compared to other rules but are less significant in the drug discovery context since they only have ancestor and descendant relationships that do not inform any mechanistic understanding of how the drug treats a disease.

例如，与其他规则相比，上面显示的规则（1）和（2）经常获得高置信度分数，但在药物发现背景下不太重要，因为它们只有祖先和后代关系，不能告知任何关于药物如何治疗疾病的机制理解。

Although rules (3), (4) and (5) scored low they contain more biologically informative relationships such as disease–gene, gene-pathway associations and so generate more compelling evidence with greater interpretability in a drug discovery context. Therefore, we considered all rules associated with a single prediction to generate evidence for the predictions, not applying any confidence score filtering in terms of rule composition.

尽管规则（3）、（4）和（5）得分较低，但它们包含更多的生物学信息关系，例如疾病-基因，基因-途径关联，因此在药物发现背景下产生了更具说服力的证据，具有更大的可解释性。因此，我们考虑了与单个预测相关的所有规则，以生成预测的证据，而不在规则组成方面应用任何置信度得分过滤。

In all experiments, known treatment relationships between the disease of interest and the drug were removed from the Knowledge graph before training the AnyBURL model on them.Evidence generation with automatic filteringOur proposed methodology utilizes automatic filtering to extract biologically meaningful evidence from a list of predictions and their corresponding rules, with a focus on identifying paths in the graph that are most relevant to the disease of interest which we call the evidence chains.

。使用自动过滤器生成证据Gour提出的方法利用自动过滤从预测列表及其相应规则中提取具有生物学意义的证据，重点是识别图中与我们称之为证据链的感兴趣疾病最相关的路径。

The complete workflow is shown in Fig. 2. The method is flexible and allows for the disabling of certain filters as needed to meet the requirements of the project, making the approach flexible and consistently applied across multiple disease projects. A path, in this context, refers to a sequence of nodes and relations that starts at a compound entity and ends at a disease entity, providing evidence that the compound can potentially be useful in treating the disease.

完整的工作流程如图2所示。该方法灵活，可以根据需要禁用某些过滤器以满足项目的要求，从而使该方法灵活且一致地应用于多个疾病项目。在这种情况下，路径是指从复合实体开始并以疾病实体结束的一系列节点和关系，提供了该化合物可能对治疗疾病有用的证据。

There are four stages to the automated filtering process startin.

自动过滤过程分为四个阶段。

Data availability

数据可用性

In Supplementary Table 1, we provide details of both public and commercial data sources used in Healx KG. CTD101, SIDER106, DrugBank102, KEGG38, OMIM109 and Pharmaprojects are commercial data sources. CTD101 data can be used only for research and educational purposes, and any Commercial users are required to purchase a license to access data from the CTD website.

。CTD101数据只能用于研究和教育目的，任何商业用户都需要购买许可证才能从CTD网站访问数据。

SIDER106 data is licenced under a creative commons Attribution-Noncommercial-Share Alike 4.0 License. For commercial use or customized versions, license should be obtained from biobyte solutions GmbH. Use and re-distribution of the content of DrugBank102 for any purpose requires a license. Academic users may apply for a free license for certain use cases and all other users require a paid license.

SIDER106数据根据知识共享署名-非商业共享-类似4.0许可证获得许可。对于商业用途或定制版本，应从biobyte solutions GmbH获得许可证。出于任何目的使用和重新分发DrugBank102的内容都需要许可证。。

KEGG38 database is available for academic use but any commercial use requires a license. Use of OMIM109 is provided free of charge to any individual for personal use, for educational or scholarly use, or for research purposes through the front end of the database. Commercial users who want to download all or part of OMIM must obtain a license by paying applicable licensing fees to and entering into a license agreement with Johns Hopkins University (JHU).

KEGG38数据库可用于学术用途，但任何商业用途都需要许可证。OMIM109的使用免费提供给任何个人，供个人使用，用于教育或学术用途，或通过数据库前端用于研究目的。想要下载全部或部分OMIM的商业用户必须通过向约翰·霍普金斯大学（JHU）支付适用的许可费并与之签订许可协议来获得许可。

Pharmaprojects comes with a commercial license granting full access to their APIs. We have shared a subgraph of the Healx KG data created for reproducibility purposes and is available in github, https://github.com/healx/automated-biological-evidence-generation-in-drug-discovery123. We have shared the experimental results from this subgraph showing a few interesting evidence chains generated for Parkinson’s disease in Supplementary Fig. 2 and the percentage of reduction achieved in Supplementary Table 5.

Pharmaprojects拥有商业许可证，可以完全访问其API。我们共享了为可重复性目的创建的Healx KG数据的子图，可在github中获得，https://github.com/healx/automated-biological-evidence-generation-in-drug-discovery123.我们分享了这个子图的实验结果，显示了补充图2中帕金森病产生的一些有趣的证据链，以及补充表5中达到的减少百分比。

The source data for this is provided as a Source Data file. The r.

其源数据作为源数据文件提供。r。

Code availability

代码可用性

The Python implementation of our methodology is available at https://github.com/healx/automated-biological-evidence-generation-in-drug-discovery123. The AnyBURL v21 Java model was trained using OpenJDK v14 and small bug fixes to this model have been made and are available in the Github repository. All downstream analyses were performed using Python 3.11, with additional packages used are ‘attrs‘, ‘click‘ and ‘numpy‘.

我们方法的Python实现可在https://github.com/healx/automated-biological-evidence-generation-in-drug-discovery123.AnyBURL v21 Java模型是使用OpenJDK v14训练的，该模型的小错误修复已经完成，可以在Github存储库中找到。所有下游分析均使用Python 3.11进行，另外使用的软件包是“attrs”、“click”和“numpy”。

Please read the README document for information on downloading and running the code..

有关下载和运行代码的信息，请阅读自述文件。。

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Gihub https://doi.org/10.5281/zenodo.10567080 (2024).Download referencesAuthor informationAuthors and AffiliationsHealx Ltd, Cambridge, United KingdomSaatviga Sudhahar, Bugra Ozer, Jiakang Chang, Wayne Chadwick, Daniel O’Donovan, Aoife Campbell, Emma Tulip, Neil Thompson & Ian RobertsAuthorsSaatviga SudhaharView author publicationsYou can also search for this author in.

Gihub公司https://doi.org/10.5281/zenodo.10567080（2024年）。下载参考文献作者信息作者和附属机构Alx Ltd，剑桥，United KingdomSaatviga Sudhahar，Bugra Ozer，Jiakang Chang，Wayne Chadwick，Daniel O'Donovan，Aoife Campbell，Emma Tulip，Neil Thompson＆Ian Robertsaatviga SudhaharView作者出版物您也可以在中搜索此作者。

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PubMed Google ScholarContributionsS.S. designed and performed research including analysis of the data with the method; B.O. performed critical review and interpretation of the results, designed the automatic filtering pipeline, curated ground truth rules for disease case studies; S.S.

。S、设计并进行研究，包括使用该方法分析数据；B、 O.对结果进行了严格的审查和解释，设计了自动过滤管道，策划了疾病病例研究的基本事实规则；S、 S。

and J.C. contributed to the creation of the software pipeline including evidence chains generation and automatic filtering; D.O’D contributed to the subgraph creation and the code repository enabling reproducibility of the results; W.C., A.C. and E.T. analysed evidence chains and preclinical experimental data for FXS and interpreted results; S.S., B.O., W.C.

J.C.为创建软件管道做出了贡献，包括证据链生成和自动过滤；D、 O为子图的创建和代码库做出了贡献，从而实现了结果的可重复性；W、 C.，A.C.和E.T.分析了FXS的证据链和临床前实验数据，并解释了结果；S、 S.，B.O.，W.C。

and I.R. wrote the paper; N.T. and I.R. reviewed all experimental results for publication.Corresponding authorCorrespondence to.

I.R.写了这篇论文；N、 T.和I.R.审查了所有实验结果以供发表。对应作者对应。

Saatviga Sudhahar.Ethics declarations

萨特维加·苏达哈尔。道德宣言

Competing interests

相互竞争的利益

The authors declare no competing interests.

作者声明没有利益冲突。

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Nature Communications thanks Krishna Bulusu, and Frank Kooy for their contribution to the peer review of this work. A peer review file is available.

《自然通讯》感谢Krishna Bulusu和Frank Kooy为这项工作的同行评审做出的贡献。同行评审文件可用。

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Reprints and permissionsAbout this articleCite this articleSudhahar, S., Ozer, B., Chang, J. et al. An experimentally validated approach to automated biological evidence generation in drug discovery using knowledge graphs.

转载和许可本文引用本文Sudhahar，S.，Ozer，B.，Chang，J。等人。一种通过实验验证的方法，使用知识图在药物发现中自动生成生物证据。

Nat Commun 15, 5703 (2024). https://doi.org/10.1038/s41467-024-50024-6Download citationReceived: 17 May 2023Accepted: 27 June 2024Published: 08 July 2024DOI: https://doi.org/10.1038/s41467-024-50024-6Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Nat Commun 155703（2024）。https://doi.org/10.1038/s41467-024-50024-6Download收到引文日期：2023年5月17日接受日期：2024年6月27日发布日期：2024年7月8日OI：https://doi.org/10.1038/s41467-024-50024-6Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

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主题

Computational modelsDrug discoveryMachine learningPsychiatric disorders

计算模型Drug发现机器学习精神疾病

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