Drug repurposing for COVID-19 via knowledge graph completion.
Drug repurposing for COVID-19 via knowledge graph completion.
复制标题
通过知识图谱完成COVID-19的药物再利用。
DOI：
10.1016/j.jbi.2021.103696
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发表时间：
2021-03
影响因子：
4.5
通讯作者：
Kilicoglu H
中科院分区：
文献类型：
--
作者：
Zhang R;Hristovski D;Schutte D;Kastrin A;Fiszman M;Kilicoglu H
使用文献衍生知识和知识图完成方法发现候选药物以重新用于COVID-19。我们提出了一种新的、综合的、基于神经网络的文献发现（LBD）方法，用于从PubMed和其他以COVID-19为重点的研究文献中识别候选药物。我们的方法依赖于使用SemRep（通过SemMedDB）提取的语义三元组。我们确定了一个信息丰富，准确的语义三元组使用过滤规则和精度分类器上开发的BERT变体的子集。我们使用这个子集来构建知识图，并应用了五种最先进的神经知识图完成算法（即，TransE、RotatE、DistMult、ComplEx和STELP）来预测药物再利用候选物。使用时间切片方法对模型进行训练和评估，并将预测的药物与文献中报告并在临床试验中评估的药物列表进行比较。这些模型得到了基于发现模式的方法的补充。基于PubMedBERT的准确分类器在识别准确语义预测方面取得了最好的性能（F1 = 0.854）。在五个知识图补全模型中，TransE的表现优于其他模型（MR = 0.923，Hits@1 = 0.417）。在文献中发现了一些与COVID-19相关的已知药物，以及其他尚未研究的药物。发现模式使得能够识别其他候选药物并产生关于候选药物与COVID-19之间联系的合理假设。其中，五种排名靠前的新药（即，紫杉醇、SB 203580、α 2-抗纤溶酶、甲氧氯普胺和氧化苦参碱），并进一步讨论了它们潜在用途的机理解释。我们表明，LBD方法不仅对于发现COVID-19候选药物是可行的，而且对于产生机制解释也是可行的。我们的方法可以推广到其他疾病以及其他临床问题。源代码和数据可在https://github.com/kilicogluh/lbd-covid上获得。
To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from PubMed and other COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative and accurate subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant. We used this subset to construct a knowledge graph, and applied five state-of-the-art, neural knowledge graph completion algorithms (i.e., TransE, RotatE, DistMult, ComplEx, and STELP) to predict drug repurposing candidates. The models were trained and assessed using a time slicing approach and the predicted drugs were compared with a list of drugs reported in the literature and evaluated in clinical trials. These models were complemented by a discovery pattern-based approach. Accuracy classifier based on PubMedBERT achieved the best performance (F1 = 0.854) in identifying accurate semantic predications. Among five knowledge graph completion models, TransE outperformed others (MR = 0.923, Hits@1 = 0.417). Some known drugs linked to COVID-19 in the literature were identified, as well as others that have not yet been studied. Discovery patterns enabled identification of additional candidate drugs and generation of plausible hypotheses regarding the links between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (i.e., paclitaxel, SB 203580, alpha 2-antiplasmin, metoclopramide, and oxymatrine) and the mechanistic explanations for their potential use are further discussed. We showed that a LBD approach can be feasible not only for discovering drug candidates for COVID-19, but also for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions. Source code and data are available at https://github.com/kilicogluh/lbd-covid.
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影响因子：
4.5
作者：
Cameron D;Kavuluru R;Rindflesch TC;Sheth AP;Thirunarayan K;Bodenreider O
通讯作者：
Bodenreider O
影响因子：
12.7
作者：
Choudhury, Abhigyan;Mukherjee, Suprabhat
通讯作者：
Mukherjee, Suprabhat
影响因子：
14.9
作者：
Chen, X;Ji, ZL;Chen, YZ
通讯作者：
Chen, YZ
DOI：
10.1038/nrg2918
发表时间：
2011-01
期刊：
Nature reviews. Genetics
影响因子：
--
作者：
通讯作者：
--
影响因子：
4.5
作者：
Cohen T;Widdows D
通讯作者：
Widdows D