By: Irving Wladawsky-Berger
April 30, 2019
Finding a new drug or innovative material traditionally has involved sophisticated guesswork, trying out many possible combinations and seeing what works—a cumbersome, time-consuming exercise of trial and error. Machine-learning algorithms are set to change that, drastically speeding up the development process for new pharmaceuticals.
The World Economic Forum included this use of artificial intelligence last year in its annual list of the top 10 emerging technologies that are set to be potentially disruptive over the next three to five years while providing significant benefits to economies and societies.
Artificial intelligence has the potential to reshape the nature of innovation and R&D, according to a 2017 paper by researchers at the National Bureau of Economic Research. Machine learning may be able to expand the set of problems that can be feasibly addressed through automation, lowering the costs of discovery across broad set of domains where classification and predictions play a major role. Molecular design is one such domain.