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Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

ABSTRACT: The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm-driven drug-ranking method was developed to prioritize those identified small molecules. The top 50 molecules were analyzed according to their molecular functions and included 11 drugs in clinical trials for treating COVID-19 and new candidates that may be of interest for clinical investigation. The CAS Biomedical Knowledge Graph provides researchers an opportunity to accelerate innovation and streamline the investigative process not just for COVID-19 but also in many other diseases.

SUBMITTER: Al-Saleem J

PROVIDER: S-EPMC8340579 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

Al-Saleem Jacob J Granet Roger R Ramakrishnan Srinivasan S Ciancetta Natalie A NA Saveson Catherine C Gessner Chris C Zhou Qiongqiong Q

Journal of chemical information and modeling 20210723 8

The COVID-19 pandemic has motivated researchers all over the world in trying to find effective drugs and therapeutics for treating this disease. To save time, much effort has focused on repurposing drugs known for treating other diseases than COVID-19. To support these drug repurposing efforts, we built the CAS Biomedical Knowledge Graph and identified 1350 small molecules as potentially repurposable drugs that target host proteins and disease processes involved in COVID-19. A computer algorithm ...[more]

PMID: 34297570

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Project description:ObjectiveTo discover candidate drugs to repurpose for COVID-19 using literature-derived knowledge and knowledge graph completion methods.MethodsWe 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.ResultsAccuracy 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.ConclusionWe 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.

Project description:BackgroundThe global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses.ObjectiveThe aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach.MethodsWe propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism.ResultsThe constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed.ConclusionsWe showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

Dataset Information

Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

Publications

Knowledge Graph-Based Approaches to Drug Repurposing for COVID-19.

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