Knowledge Graphs for Drug Discovery: Transforming Target Identification and Biomedical Data Integration

Leveraging Relationships in Knowledge Graphs to Enhance Drug Discovery and Target Identification

Relational databases for scientific data management

Currently, the search for targets in drug repositioning and new targets for disease treatment requires integrating complex biological and medical data sets. These include multi-scale omics data sets with genomic, proteomic, or metabolomic information (big data) and clinical data that need proper integration. To store and integrate heterogeneous datasets within biomedical research, researchers have habitually and successfully used relational databases. In these databases, information is organized in structured thematic tables with identifiers connecting these tables. This model allows for large volume storage, easy expansion, secure access, and well-defined usage rules [1]. It has been a standard paradigm in computer data management since its inception in the 1970s by Edgar Frank Codd at IBM Laboratories. Moreover, it has been used across various scientific, technological, and industrial fields.

The emergence of networks: inferring knowledge from interactions

Advances in computing and the alliance between biology and mathematics have led to a revolution in data management, structuring, and analysis. Many biological and biomedical challenges inspire new mathematics, offering solutions and theoretical approaches to quantify and infer knowledge that otherwise would remain hidden [2]. A prime example is the use of networks in biology. A network or graph is a data set composed of vertices or nodes (concepts) and edges (relationships between nodes). Systems biology, a highly interdisciplinary branch that has developed remarkably over more than 20 years, employs networks as a primary method in its holistic approach to biological processes. Biological processes interact internally and externally within cells, tissues, or organs. Therefore, studying interrelationships and connectivity is a hallmark of biological systems.

Biological applications of networks

Today, biological networks are a common representation model, providing insights into the interactive and dynamic complexity of biological processes. Well-known examples include gene regulation and coexpression networks (comprising transcription factors, genes, and associated proteins), intracellular and extracellular signaling networks, metabolic networks (relationships between chemical compounds), and protein-protein interaction networks (with protein entities as nodes and their interactions as links). These methodologies have enabled the identification of gene clusters underlying pathological mechanisms and important disease-associated mutations [3-7].

A paradigm shift in scientific data management: the knowledge graphs

The benefits of graphs in fields where entity relationships are prominent led to the use of graphs as databases. This resulted in the emergence of knowledge graphs (KGs). A KG is a database model based on graph architecture, incorporating information from different entities, their relationships, and associated metadata. KGs facilitate network analysis, enabling the identification of operational modules, the measurement of element relevance, detection of variations in the modeled knowledge scheme, and derivation of data insights from previously unknown connections [8-11]. Today, KGs are favored for their flexible structure, fast query retrieval, intuitive nature, and ease of use, as demonstrated by Google's incorporation of KG into its search engine tools.

At ZeClinics, we work with public and proprietary biomedical datasets, employing the appropriate computational technology to construct a company knowledge graph. This enhances our analytical strategy for identifying new protein and/or gene targets and drug discovery. We believe the benefits of knowledge graphs as databases significantly surpass those of relational databases for targeting purposes.

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