Drug repositioning, also known as drug repurposing, is the process of identifying new therapeutic uses for existing drugs. Knowledge graphs can be used to support this process by storing and analyzing large sets of data on drugs, diseases, and the relationships between them. In drug repositioning, knowledge graphs can be used to represent the complex relationships between drugs, target proteins, disease symptoms, and other related entities. For example, a node representing a drug can be connected to nodes representing the target protein it binds to, the disease it is currently used to treat, and any related side effects. This allows researchers to easily explore the connections between different entities and identify potential new therapeutic uses for the drug. Graph databases can also be used to store and analyze large sets of data from high-throughput experiments, such as genomics and proteomics data. This can help researchers understand the underlying biology of a disease and identify potential new therapeutic targets for a drug. Additionally, graph databases can be used to integrate data from various sources such as scientific literature, patent information, and external databases. This can help researchers stay up to date with the latest developments in their field and identify potential new therapeutic uses for drugs. The use of knowledge graphs in drug repositioning can help researchers more effectively analyze and understand complex data sets, identify potential new therapeutic uses for existing drugs, and accelerate the drug development process.

Clinical trials are studies aiming at determining the safety and efficacy of interventions, treatments, or investigational drugs on human subjects. Effective and successful clinical trials are essential in developing new drugs and advancing new treatments. The average cost of a single phase in clinical trials ranges from 1.4 million up to 52.9 million US dollars. In addition, the success rate of clinical trials is considerably low. As reported for certain therapeutic groups like Oncology, the overall success rate of clinical trials could be as low as 3.4%. The high cost and low success rate of clinical trials motivate deliberate analysis of existing clinical trials, inferring knowledge from them, utilizing existing clinical trials in innovative ways, and accordingly carefully designing future clinical trials has been proven to be an effective representation of knowledge inference purposes. Constructing a KG over clinical trial data is vital for advancing the analysis and research of clinical trials. Any data representing medical entities such as studies, drugs, conditions, drugs used in studies, adverts events, and outcomes. Are fused with the knowledge to gain rich information using Graph tech enabling more biomedical applications (e.g., adverse drug event prediction, outcome prediction) than the existing knowledge base in clinical trials. This results in demonstrating its potential utilities in various applications such as drug repurposing and similarity search, embedded analysis & other application uses like drug event prediction & outcome prediction much more.

API (Active Pharmaceutical Ingredient) manufacturing is the process of producing the active ingredient in a pharmaceutical drug. The API is part of the drug that provides the intended therapeutic effect. The manufacturing process for an API can vary depending on the type of drug and its intended use.API manufacturing is a complex and highly regulated process that requires a significant investment in equipment, facilities, and personnel. The quality of the API is crucial for the safety and efficacy of the drug, leaving less time for in-depth analysis. It includes several steps for API manufacturing like synthesis, purification, characterization, formulation, quality control, and packaging. Here, the use of knowledge graphs in API manufacturing can help pharmaceutical companies improve the efficiency and effectiveness of their manufacturing processes, ensure regulatory compliance, improve supply chain management, and support knowledge management.

Supply chain visibility refers to the ability to track the movement of goods and materials through the supply chain, from the manufacturer to the end consumer. Knowledge graphs can be used to support this process by storing and analyzing large sets of data on the movement of goods, suppliers, and the relationships between them. In supply chain visibility, knowledge graphs can be used to represent the complex relationships between products, suppliers, transportation, and other related entities. Graph databases can also be used to store and analyze large sets of data from the supply chain, such as inventory levels, shipping information, and production data. This can help companies understand the flow of goods through the supply chain, identify bottlenecks, and make decisions about where to invest in improvements. Graph databases can be used to integrate data from various sources such as sensor data, electronic records, and external databases. Knowledge graphs can be used to support the implementation of SCOR by storing huge multi-dimensional data. Using knowledge graphs in SCOR can help companies more effectively analyze and understand complex datasets, identify potential issues in the supply chain, and make more informed decisions about supply chain operations. This can help to improve supply chain efficiency and support compliance with regulatory requirements