What's Up after AlphaFold on ML for Structural Biology?

AlphaFold 2, the AI-based program developed by Google's Deepmind to crack the problem of predicting protein structures, made a strike in late 2020 when it "won" the 14th edition of a biannual "contest" on protein structure prediction called CASP (Critical Assessment of Structure Prediction) presented its results. It then made a second strike half a year later when Deepmind published a peer-reviewed article in the journal Nature describing how AlphaFold 2 works, and released its code openly in GitHub and as a Google Colab notebook that everybody could use. The hype kept growing as scientists developed even better notebooks from it, and as they found the many applications that AlphaFold had, even beyond its original aim. This hype grew even further when Deepmind released a new version of AlphaFold better suited to modeling the complexes made by multiple proteins when they interact. Then again when Deepmind joined forces with the European Institute of Bioinformatics to release a database of 3D models for all known proteins.