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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. 

Things like gene editing, stem cells, immunotherapies and new types of biologics are now mega-trends in the pharmaceutical industry, widely covered in media, and I guess there is little doubt that biology is the next big thing in medicine. However, in this post I would like to outline several hot areas in small molecule drug discovery, suggesting a lot of untapped potential and investment prospects in this more “traditional” pharmaceutical research space.

A historically significant milestone has just been reached by Oxford, UK-based Exscientia, which used its artificial intelligence (AI)-based drug discovery platform Centaur Chemist™ to deliver the first drug candidate in the framework of their multiyear collaboration with GSK. This AI-derived small molecule is a highly potent in vivo active substance targeting a novel pathway for the treatment of chronic obstructive pulmonary disease (COPD). The milestone is among first early successes in drug discovery associated with the recent adoption of novel machine learning/AI methods and workflows.

In 1970-80s, the idea of virtual screening was regarded as a conceptual way to substitute costly and time-consuming experimental “screen-everything-you-have” approaches with a much faster and cheaper predictive modelling to cherry-pick only the best molecules for subsequent synthesis and validation in a lab. A great number of computational tools and approaches emerged, aiming at “pre-screening” new promising molecules, so called “hits”, or augmenting experimental screening programs to optimize efforts.

The first month of 2019 is not over yet, but there are already four major announcements about new research projects between large drug discovery corporations and smaller artificial intelligence (AI) companies -- this is more than the number of all similar announcements in the year 2014 combined -- only three.