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Case Study

A better way to prioritize drug targets

Evaluate large libraries of therapeutic candidates against published literature, patents and clinical trials to prioritize the best targets for drug development


A cancer therapeutics company has a high volume platform to identify novel drug targets. Once identified, their scientists need to know if a target is, novel, if it has been previously studied, has been tested in a prior clinical trial, and if it is the subject of any patent activity. The challenge was to create a data-driven process to prioritize which therapeutic candidates should move forward in the drug development pipeline. A platform is needed to analyze large libraries of therapeutic candidates against the body of scientific literature and public data to provide context and insights.


Our team trained NLP models to extract gene to disease relationships from ~2 TB of scientific literature, clinical trials, and patents to create a target evaluation platform, enabling their scientists to prioritize the highest quality targets for further research. As a result, our clients were able to sign collaboration agreements with new pharma partners based on the quality of the drug targets as highlighted in the data platform combined with pre-clinical data.

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