From the outset of the drug discovery pipeline, I2E can help you understand which areas have already attracted heavy investment and which offer potential for new drug candidates.
This information can then be used to maximize profit potential.
"the agility and flexibility of [Linguamatics I2E] NLP-based querying is remarkable. Its uses in text analysis are practically unlimited; in our project we take advantage of ontologies to categorize patent documents." - Sanofi US Global Patents Department
According to our customers, I2E dramatically reduces the research time required – for example, from weeks or months to a matter of days. I2E’s advanced knowledge discovery approach can save tens of millions of investment dollars.
Patents can be hundreds of pages long and contain complex information constructions and interconnected facts. But life science organizations need a thorough understanding of the patent landscape, in terms of freedom to operate for new lead compounds in R&D; opportunities for extending patent cover to defend against generic competition; and to detect potential infringement against their own IP.
A traditional keyword search/document retrieval approach can be incomplete, and returns documents that you still need to read.. Because a Natural Language Processing approach involves understanding the meaning of the text, text mining using I2E enables a rapid and deep analysis of patent documents, including the important facts and the context around them, to answer the key questions – potentially saving millions of dollars.
I2E can search for numerical information, chemicals by name or structure, classes of drug targets or therapeutic area, focus the search on specific regions of the patent documents, or follow claim chains across a patent.