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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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Research Progresses and Applications of Knowledge Graph Embedding Technique in Chemistry
Chuanghui Wang1, Yunqing Yang1, Jinshuai Song2
1School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China.
Journal of Chemical Information and Modeling
|September 20, 2024
Summary
Knowledge graphs (KGs) and knowledge graph embeddings (KGEs) organize chemical data for better analysis. These techniques improve chemical property prediction and drug discovery by integrating complex information.
Area of Science:
- Computational Chemistry
- Cheminformatics
- Bioinformatics
Background:
- Knowledge graphs (KGs) model entities and their interrelations.
- Knowledge graph embedding (KGE) represents these entities and relationships in a vector space.
- KGE facilitates dense and efficient data representation for chemical applications.
Purpose of the Study:
- To review classical and contemporary KGE methodologies.
- To catalog chemical databases for KG construction.
- To explore KG and KGE applications in chemistry.
Main Methods:
- Review of distance-based, semantic matching, and neural network-based KGE models.
- Identification of key chemical and biochemical databases for KG generation.
- Analysis of current KG and KGE applications in chemistry.
Main Results:
- Comprehensive overview of KGE techniques.
- Listing of essential databases for chemical KG construction.
- Exploration of applications in risk assessment, property prediction, and drug discovery.
Conclusions:
- KG and KGE techniques offer a coherent framework for chemical data.
- These methods enhance chemical data representation and downstream task performance.
- Discussion of challenges and future directions for KG and KGE in chemistry.
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