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  • Writer's pictureKeith Elliston

Exploring the Potential of Drug Repurposing using Knowledge Graphs and Graph Analytics

Drug repurposing, also known as drug repositioning, is the process of identifying new therapeutic uses for existing drugs. This approach has the potential to speed up drug development and reduce costs, as drugs that have already been approved for one indication can be repurposed for others without the need for extensive preclinical and clinical trials. In recent years, knowledge graphs and graph analytics have emerged as powerful tools for drug repurposing.


A knowledge graph is a structured representation of knowledge, where entities (such as drugs and diseases) are represented as nodes and relationships between them are represented as edges. Knowledge graphs can be used to represent a wide range of information, including drug-disease relationships, drug-drug interactions, and gene-disease relationships.


Graph analytics is the process of analyzing knowledge graphs to extract insights and make predictions. One of the main advantages of graph analytics is that it can take into account the rich, complex relationships between entities in a knowledge graph. This can be particularly useful for drug repurposing, as it can help identify potential new uses for existing drugs based on their relationships to other drugs and diseases.


One example of how knowledge graphs and graph analytics have been used for drug repurposing is the study by Liu et al. (2019). In this study, the authors built a knowledge graph of drug-disease relationships using data from multiple sources, including the DrugBank and DisGeNET databases. They then used graph analytics to identify drugs that were likely to be effective for certain diseases based on their relationships to other drugs and diseases. They found that their approach was able to predict drug-disease relationships with high accuracy, and that it identified several drugs that had not previously been associated with the diseases they were predicting.


Another example is the study by Wang et al. (2018) in which the authors used knowledge graph and network analysis to identify potential new indications for existing drugs. They built a knowledge graph of drug-disease relationships using data from the DrugBank and DisGeNET databases, and then used network analysis to identify drugs that were likely to be effective for certain diseases based on their relationships to other drugs and diseases. They found that their approach was able to identify several drugs that had not previously been associated with the diseases they were predicting.


In conclusion, knowledge graphs and graph analytics are powerful tools for drug repurposing. They can be used to represent a wide range of information, including drug-disease relationships, drug-drug interactions, and gene-disease relationships. Graph analytics can then be used to extract insights and make predictions based on the rich, complex relationships between entities in a knowledge graph. These approaches have been successfully applied in several studies, and have the potential to speed up drug development and reduce costs by identifying new therapeutic uses for existing drugs.


References:


Liu, Y., Wang, S., Chen, X., & Chen, L. (2019). Drug repositioning via heterogeneous graph embedding. PloS one, 14(5), e0216366.

Wang, X., Li, Y., Li, X., & Liu, X. (2018). Drug repositioning by network-based inference of potential indications. Bioinformatics, 34(16), i818-i826.


For more information, contact Ingentium (info@ingentium.com, https://www.ingentium.com)

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