Accelerating Drug Discovery with TypeDB

The life sciences industry has traditionally relied on bespoke and idiosyncratic techniques to identify new drug candidates for investigation and validation. This is inefficient as a tremendous amount of financial resources are expended at each stage of the research trials, with success far from assured at each stage.

As an alternative, researchers have begun leveraging data management techniques in attempt to more systematically identify and uncover new candidates. However, these knowledge engineering attempts have been, until now, limited by the tremendous amount of heterogeneous data, which is difficult to integrate due to its complex nature and rich semantics. With TypeDB, researchers and engineers can make use of strong typing to streamline data ingestion and automate modeling, ingesting, and linking the data and creation of semantic connections.

In this webinar, we'll look at how TypeDB can be used to model complex biological relationships to accelerate the drug discovery process. We'll explore simplifying the ingestion of disparate data with rule-inference, identifying high-level relationships by abstracting over direct connections with deep reasoning chains, and then see how this enables us to find candidate drug targets for a sample disease.

Join this webinar, and learn more about:

• Integration of disparate data sources.

• Exposing deeply buried connections.

• Identifying potential drug targets.