Protein-folding models are the success story in AI for science.

In the late 2010s, researchers from Google DeepMind used machine learning to predict the three-dimensional shape of proteins. AlphaFold 2, announced in 2020, was so good that its creators shared the 2024 Nobel Prize in chemistry with an outside academic.

Yet many academics have had mixed feelings about DeepMind’s advances. In 2018, Mohammed AlQuraishi, then a research fellow at Harvard, wrote a widely read blog post reporting on a “broad sense of existential angst” among protein-folding researchers.

The first version of AlphaFold had just won CASP13, a prominent protein-folding competition. AlQuraishi wrote that he and his fellow academics worried about “whether protein structure prediction as an academic field has a future, or whether like many parts of machine learning, the best research will from here on out get done in industrial labs, with mere breadcrumbs left for academic groups.”

Industrial labs are less likely to share their findings fully or investigate questions without immediate commercial applications. Without academic work, the next generation of insights might end up siloed in a handful of companies, which could slow down progress for the entire field.