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• W3093150592 endingPage "103696" @default.
• W3093150592 startingPage "103696" @default.
• W3093150592 abstract "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." @default.
• W3093150592 created "2020-10-22" @default.
• W3093150592 creator A5016571803 @default.
• W3093150592 creator A5044886642 @default.
• W3093150592 creator A5052103895 @default.
• W3093150592 creator A5062552700 @default.
• W3093150592 creator A5065037360 @default.
• W3093150592 creator A5088626648 @default.
• W3093150592 date "2021-03-01" @default.
• W3093150592 modified "2023-10-16" @default.
• W3093150592 title "Drug repurposing for COVID-19 via knowledge graph completion" @default.
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