With new and exciting technologies, it often happens that one particular application or use case leads the way initially… and then, when the euphoria turns into commercial reality, people start looking at other applications where the new technology can also bring value. In text mining, the same holds true. Pharma companies have now been using NLP text mining technologies for many years, in areas such as target validation, gene-disease associations, clinical trial optimization, and patent analytics, for example. As they become comfortable and, indeed, expert in these areas, attention has turned to areas where the core technology needs to be adapted or tweaked to meet a specific requirement.
For example, when looking to apply NLP to the time-consuming and costly business of discovering new, novel compounds, users hit a significant issue; trying to understand every single component part of some of the long chemical names. Not an insurmountable problem, but one that needed time, expertise and determination.