Managing Pharma Research
The process of creating pharmaceuticals typically involves tracking the evolution of everything from genetic patterns, organisms and enzymes, testing regimens and results, through various clinical trials. Knowledge graphs make it possible to model each of these and adapt to changing conditions, and can frequently also be used to identify new avenues for exploration.
Ensuring Regulatory Compliance and Managing Risks
Knowledge graphs frequently are used for content and digital asset management systems, and moreover provide the ability to annotate tests, resources, and events. With SHACL, such systems can also be set up to identify common compliance issues, reducing the risks of both audits and project retrenchment.
Improve Modeling Efficiency and Explainability
As machine learning models become more sophisticated, the need for managing the labeling process grows over time. Knowledge Graphs can serve to embed keys into models that can identify, from the results of these models, how the results were derived, providing an opportunity not only to reduce unnecessary processes, but also providing insights into why the results looked the way they did, something that's very difficult to do with machine learning algorithms ordinarily.
Semantic Discovery Can Power Innovation
Because semantic systems are able to build logical inferencing into models, they can be used with pharmaceutical research in particular to identify relationships that may be ordinarily hidden, providing insights into the research and testing process that can translate to new drug and interaction discoveries.