Case Study: Pfizer accelerates RNAi target prioritization with Linguamatics NLP

Using Linguamatics NLP for Target Prioritization at Pfizer

Pfizer's RNAi therapeutics research group faced the challenge of rapidly identifying genes amenable to RNAi treatment by extracting specific data from scientific literature, a task too vast for manual review. They turned to the vendor Linguamatics and its NLP text mining platform to automate this process and efficiently query large document collections to find relationships between gene silencing and disease amelioration.

Linguamatics implemented a solution using powerful NLP queries, custom vocabularies, and domain ontologies to extract and structure relevant facts from public literature. This allowed Pfizer researchers to rapidly compile comprehensive target profiles, categorize candidates, and integrate the mined data with over ten other knowledge sources. The result was a high-quality, curated shortlist of targets for therapeutic development, achieved in a matter of weeks and providing scientists with a single, actionable overview that was impossible to create with traditional search engines.

Pfizer

Phoebe Roberts

Senior Principal Scientist