Andrew D. Blevins and Ian K. Quigley, co-founders of Leash Bio, released a pre-print last year that I think deserves some more attention. Using a Bemis-Murcko scaffold split, the authors show that it is possible to train a machine learning model to identify which of 1,815 medicinal chemists produced a molecule from its structure alone, achieving a top 5 accuracy of 60% and a top 1 of 27%.

Let’s take a step back. Why aim to predict who made a molecule in the first place? The authors motivate this well, drawing on the story of Clever Hans. Owned and showcased by Wilhelm von Osten in 20th century Germany, Clever Hans was a horse purportedly able to solve mathematical problems, stamping out solutions with his hoof. However, it was quickly realized that Clever Hans could not actually perform arithmetic, but rather read the micro-expressions and posture of those posing the question, stopping his count when the faces of observers revealed the right answer had been reached. The moral? Clever Hans learned how to predict a correlate of a task, which was mistaken for mastery of the task itself.