Potential Target Discovery and Drug Repurposing for Coronaviruses : Study Involving a Knowledge Graph-Based Approach
©Pei Lou, An Fang, Wanqing Zhao, Kuanda Yao, Yusheng Yang, Jiahui Hu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2023..
BACKGROUND: The 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.
OBJECTIVE: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach.
METHODS: We 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.
RESULTS: The 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, Hits10=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.
CONCLUSIONS: We 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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:25 |
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| Enthalten in: |
Journal of medical Internet research - 25(2023) vom: 20. Okt., Seite e45225 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lou, Pei [VerfasserIn] |
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| Links: |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
Date Completed 26.10.2023 Date Revised 12.11.2023 published: Electronic Citation Status MEDLINE |
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| doi: |
10.2196/45225 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM363544119 |
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| 100 | 1 | |a Lou, Pei |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Potential Target Discovery and Drug Repurposing for Coronaviruses |b Study Involving a Knowledge Graph-Based Approach |
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| 500 | |a Date Revised 12.11.2023 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a ©Pei Lou, An Fang, Wanqing Zhao, Kuanda Yao, Yusheng Yang, Jiahui Hu. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.10.2023. | ||
| 520 | |a BACKGROUND: The 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 | ||
| 520 | |a OBJECTIVE: The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach | ||
| 520 | |a METHODS: We 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 | ||
| 520 | |a RESULTS: The 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, Hits10=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 | ||
| 520 | |a CONCLUSIONS: We 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a COVID-19 | |
| 650 | 4 | |a coronavirus | |
| 650 | 4 | |a drug repurposing | |
| 650 | 4 | |a heterogeneous data integration | |
| 650 | 4 | |a interpretable prediction | |
| 650 | 4 | |a knowledge graph embedding | |
| 700 | 1 | |a Fang, An |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Wanqing |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Kuanda |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Yusheng |e verfasserin |4 aut | |
| 700 | 1 | |a Hu, Jiahui |e verfasserin |4 aut | |
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