Driving Increased Efficiency in Pharmaceuticals

The estimated cost for drug development by U.S. biopharmaceutical companies is nearly $ 1 billion per drug. Instead of throwing darts at the wall and hoping to land on an eventual hit--an expensive and inefficient process--pharmaceutical companies can leverage machine learning techniques to not only cull through literature and journal publications using (again) NLP but also to pre-screen for the most effective potential compounds to prioritize their time. Pfizer's researchers use natural language processing to analyze over a million articles in medical journals, 20 million abstracts of journal articles, and 4 million patents. Computational biochemistry allows drug-makers to cut out a significant portion of the test tube experiments. Instead, a computer simulates the protein and tests all of its atomic interactions.