Organic chemists should place their trust in machine learning’s black box

Classically, a black box is a system whose inputs are controlled or known, and whose outputs can be harvested, but the internal workings remain a mystery. Take Google search – we may know roughly how it works, but details of the search algorithm are kept secret from the public. But when organic chemistry meets computing, we sometimes feel we want to know everything – black boxes can be seen as a frustrating and distrusted tool.

It’s fair to say that sometimes, comprehensive understanding lets us control all variables to avoid problems. As a student, I expressed concern over the results of a computational exercise, to be dismissed with ‘but the computer says this is what you have to do’. Three months of hard work later, we synthetic chemists were vindicated when it was found that thanks to a computing error in a system we couldn’t access directly, we had indeed been working on the wrong compounds for all that time. I have had a rigorous dose of scepticism for methods out of our control ever since!