论文标题
通过知识图完成的COVID-19的药物重新利用
Drug Repurposing for COVID-19 via Knowledge Graph Completion
论文作者
论文摘要
目的：使用文学衍生的知识和知识图完成方法发现候选药物以重新利用Covid-19。方法：我们提出了一种基于基于神经网络的新型，综合和神经网络的发现方法（LBD）方法，以鉴定PubMed和Covid-19焦点研究文献的候选药物。我们的方法依赖于使用SEMREP提取的语义三元三元素（通过SEMMedDB）。我们使用过滤规则和在BERT变体上开发的精确分类器确定了语义三元组的信息子集，并使用此子集来构建知识图。使用五种SOTA，神经知识完成算法来预测药物重新利用候选者。使用时间切片方法对模型进行了训练和评估，并将预测的药物与文献中报道的药物列表进行了比较，并在临床试验中进行了评估。这些模型通过基于发现模式的方法进行了补充。结果：基于PubMedbert的精度分类器在分类语义谓词中实现了最佳性能（F1 = 0.854）。在五个知识图完成模型中，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，以及一些尚未研究的候选药物。发现模式可以生成有关候选药物与Covid-19之间关系的合理假设。其中进一步讨论了其中的五种高度排名和新颖的药物（紫杉醇，SB 203580，α2-抗血清素，吡咯烷二硫代氨基甲酸吡啶甲酸二吡啶和丁基化的羟基甲酚）。结论：我们表明，LBD方法对于发现Covid-19的药物候选物以及生成机械解释是可行的。我们的方法可以推广到其他疾病以及其他临床问题。
Objective: To discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods. Methods: We propose a novel, integrative, and neural network-based literature-based discovery (LBD) approach to identify drug candidates from both PubMed and COVID-19-focused research literature. Our approach relies on semantic triples extracted using SemRep (via SemMedDB). We identified an informative subset of semantic triples using filtering rules and an accuracy classifier developed on a BERT variant, and used this subset to construct a knowledge graph. Five SOTA, neural knowledge graph completion algorithms were used 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. Results: Accuracy classifier based on PubMedBERT achieved the best performance (F1= 0.854) in classifying 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 some candidate drugs that have not yet been studied. Discovery patterns enabled generation of plausible hypotheses regarding the relationships between the candidate drugs and COVID-19. Among them, five highly ranked and novel drugs (paclitaxel, SB 203580, alpha 2-antiplasmin, pyrrolidine dithiocarbamate, and butylated hydroxytoluene) with their mechanistic explanations were further discussed. Conclusion: We show that an LBD approach can be feasible for discovering drug candidates for COVID-19, and for generating mechanistic explanations. Our approach can be generalized to other diseases as well as to other clinical questions.
