# Drug Repurposing for COVID-19 via Knowledge Graph Completion

arXiv: https://arxiv.org/abs/2010.09600 · HTML: https://mlsanity.com/paper/2010.09600/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2020.

## Built on by (most cited first, 30 of 30)

- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine](https://mlsanity.com/paper/2405.00465/) (2024)
- [Neurosymbolic AI for Reasoning over Knowledge Graphs: A Survey](https://mlsanity.com/paper/2302.07200/) (2023)
- [BERT Based Clinical Knowledge Extraction for Biomedical Knowledge Graph Construction and Analysis](https://mlsanity.com/paper/2304.10996/) (2023)
- [How far is Language Model from 100% Few-shot Named Entity Recognition in Medical Domain](https://mlsanity.com/paper/2307.00186/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [BIOS: An Algorithmically Generated Biomedical Knowledge Graph](https://mlsanity.com/paper/2203.09975/) (2022)
- [Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing](https://mlsanity.com/paper/2202.08712/) (2022)
- [KG-Hub -- Building and Exchanging Biological Knowledge Graphs](https://mlsanity.com/paper/2302.10800/) (2023)
- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [W-procer: Weighted Prototypical Contrastive Learning for Medical Few-Shot Named Entity Recognition](https://mlsanity.com/paper/2305.18624/) (2023)
- [Neurosymbolic AI for Reasoning on Biomedical Knowledge Graphs](https://mlsanity.com/paper/2307.08411/) (2023)
- [Discovering novel drug-supplement interactions using a dietary supplements knowledge graph generated from the biomedical literature](https://mlsanity.com/paper/2106.12741/) (2021)
- [Developing a Knowledge Graph Framework for Pharmacokinetic Natural Product-Drug Interactions](https://mlsanity.com/paper/2209.11950/) (2022)
- [Benchingmaking Large Langage Models in Biomedical Triple Extraction](https://mlsanity.com/paper/2310.18463/) (2023)
- [Benchmarking and Analyzing In-context Learning, Fine-tuning and Supervised Learning for Biomedical Knowledge Curation: a focused study on chemical entities of biological interest](https://mlsanity.com/paper/2312.12989/) (2023)
- [Accelerating COVID-19 research with graph mining and transformer-based learning](https://mlsanity.com/paper/2102.07631/) (2021)
- [Repurposing of Resources: from Everyday Problem Solving through to Crisis Management](https://mlsanity.com/paper/2109.08425/) (2021)
- [Analysis of Drug repurposing Knowledge graphs for Covid-19](https://mlsanity.com/paper/2212.03911/) (2022)
- [How Important is Domain Specificity in Language Models and Instruction Finetuning for Biomedical Relation Extraction?](https://mlsanity.com/paper/2402.13470/) (2024)
- [HGTDR: Advancing Drug Repurposing with Heterogeneous Graph Transformers](https://mlsanity.com/paper/2405.08031/) (2024)
- [Research on the Proximity Relationships of Psychosomatic Disease Knowledge Graph Modules Extracted by Large Language Models](https://mlsanity.com/paper/2412.18419/) (2024)
- [Recent Advances and Future Directions in Literature-Based Discovery](https://mlsanity.com/paper/2506.12385/) (2025)
- [How Sharp and Bias-Robust is a Model? Dual Evaluation Perspectives on Knowledge Graph Completion](https://mlsanity.com/paper/2512.06296/) (2025)
- [Natural Language Processing for Drug Discovery Knowledge Graphs: promises and pitfalls](https://mlsanity.com/paper/2310.15572/) (2023)
- [Extending TWIG: Zero-Shot Predictive Hyperparameter Selection for KGEs based on Graph Structure](https://mlsanity.com/paper/2412.14801/) (2024)
- [RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases](https://mlsanity.com/paper/2510.05764/) (2025)
- [Generalized Rank-based Evaluation for Knowledge Graph Completion: Perspectives, Framework, and Analyses](https://mlsanity.com/paper/2606.08921/) (2026)
- [PROBE-Web: An Interactive System for Probing Evaluation Landscapes of Knowledge Graph Completion Models](https://mlsanity.com/paper/2606.08926/) (2026)
- [ANCHOR-RE: An Agentic Neuro-Symbolic Framework for Grounded Biomedical Relation Extraction](https://mlsanity.com/paper/2608.03154/) (2026)

## Abstract (arXiv)

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.