The newly organized research project “MELLODDY” (Machine Learning Ledger Orchestration for Drug Discovery), involving ten large pharma companies and seven technology providers, is that kind of deals which can catalyze a transition of the pharmaceutical industry to a new level -- a “paradigm shift”, as one might refer to it in terms of Thomas Kuhn’s “The Structure of Scientific Revolutions”.
The project aims at developing a state-of-the-art platform for collaboration, based on Owkin’s blockchain architecture technology, which would allow collective training of artificial intelligence (AI) algorithms using data from multiple direct pharmaceutical competitors, without exposing their internal know-hows and compromising their intellectual property -- for the collective benefit of everyone involved.
While artificial intelligence (AI) already proved to be a groundbreaking thing in many industries (robotics, finance, surveillance, cyber security, self-driving cars to name just a few), drug discovery still seems like a hard case for machine learning practitioners. A major reason for that is the lack of quality data to train models properly.
It might seem surprising, as pharmaceutical research generates enormous amounts of data daily. But when you consider a degree of secrecy and protectionism that competing pharmaceutical giants put on their research, it becomes clear that majority of data is actually not available for training, it is dispersed across hundreds of organizations, hidden behind their firewalls. Decades of screening, testing and validation research -- combined data from largest pharma companies might well be enough to make AI really smart at predicting next drug candidates. But how to deal with secrecy and competition, how to share data for the collective benefit without compromising own know how?
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Topics: Emerging Technologies