Deep learning uses algorithms, often neural networks that are trained on large amounts of data, to extract information from new data. It is very different from traditional computing with its step-by-step instructions. Rather, it learns from data. Deep learning is far less transparent than traditional computer programming, leaving important questions—what has the system learned, what does it know?

As a chemistry professor I like to design tests that have at least one difficult question that stretches the students' knowledge to establish whether they can combine different ideas and synthesize new ideas and concepts. We have devised such a question for the poster child of AI advocates, AlphaFold, which has solved the protein-folding problem.

Protein folding

Proteins are present in all living organisms. They provide the cells with structure, catalyze reactions, transport small molecules, digest food and do much more. They are made up of long chains of amino acids like beads on a string. But for a protein to do its job in the cell, it must twist and bend into a complex three-dimensional structure, a process called protein folding. Misfolded proteins can lead to disease.

In his chemistry Nobel acceptance speech in 1972, Christiaan Anfinsen postulated that it should be possible to calculate the three-dimensional structure of a protein from the sequence of its building blocks, the amino acids.