Here’s a story that evangelists for so-called AI (artificial intelligence) – or machine-learning (ML) – might prefer you didn’t dwell upon. It comes from the pages of Nature Machine Intelligence, as sober a journal as you could wish to find in a scholarly library. It stars four research scientists – Fabio Urbina, Filippa Lentzos, Cédric Invernizzi and Sean Ekins – who work for a pharmaceutical company building machine-learning systems for finding “new therapeutic inhibitors” – substances that interfere with a chemical reaction, growth or other biological activity involved in human diseases.
The essence of pharmaceutical research is drug discovery. It boils down to a search for molecules that may have therapeutic uses and, because there are billions of potential possibilities, it makes searching for needles in haystacks look like child’s play. Given that, the arrival of ML technology, enabling machines to search through billions of possibilities, was a dream come true and it is now embedded everywhere in the industry.
