Data-driven NLP plus machine learning equals better drug discovery insights

AI Siblings: NLP and Machine Learning for Better Drug Discovery

October 13 2017

There’s growing interest in the use of machine learning to solve challenges across the drug-discovery pipeline within the biopharmaceutical community. The availability of high quality data for training algorithms is vital to machine learning success - but much of this information is tied up in unstructured, or semi-structured text sources. Natural language processing (NLP) is the key to extracting the wealth of data hidden in unstructured text, and Linguamatics’ customers have been finding out first-hand what this approach can do for them.

Using Linguamatics I2E NLP text mining:

  • Eli Lilly researchers mine adverse event data to identify potential new uses for existing drugs.
  • A top-10 pharma company process and understand unstructured “voice of the customer” call feeds, to categorize the feeds and help build predictive models.
  • Roche and Humboldt University of Berlin identified MEDLINE abstracts containing both the protein target and specific disease indication of a known set of cancer therapeutics, and applied machine learning to predict the success or failure of drugs in Phase II or III with high accuracy.

Read the full 'Data-driven NLP plus machine learning' application note to find out more about how NLP can support effective machine learning projects.

Access the application note

Learn more about I2E and Machine Learning with this video.