Pharma and healthcare companies are data rich organizations; however a large proportion of this data isn't as accessible as desired, because the information is locked in unstructured text. Linguamatics I2E is used by our customers across drug discovery, development and delivery of therapeutics to tackle this problem, and our biannual user conferences are always a great way to hear updates on applications of text mining and best practise to help solve these data access challenges.
Last month, we had speakers presenting on where they are getting value from I2E, from bench to bedside. Attendees from the pharmaceutical industry, biotech, healthcare, academia, and partner vendor companies came to our Spring Text Mining Conference, for training sessions, networking, discussions, and of course, excellent presentations and talks.
Structure Activity Relationships from Patents, for Medicinal Chemistry
Starting in early discovery, Ortrud Steinfuehr (Information Manager Bayer AG) presented on “An Approach to provide SAR Data from Patents”. Structure Activity Relationships (SAR) provide information about how the 3D structure of a chemical compound impacts biological activity, such as effective dose or inhibitory concentration values for specific targets. SAR is very valuable for medicinal chemistry research around optimisation and modification of lead compounds. These data are often in patents, typically written in a way to make automated extraction of SAR very tricky.