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Guide to my blog articles on AlphaFold

Summary of my Towards Data Science articles discussing the most important developments ever since AlphaFold 2’s code and papers were released.

4 min readOct 13, 2021

Since the Nature paper on AlphaFold 2 and its code came out in July 2021, other works have quickly developed along various lines: from programs to run AlphaFold easily, to databases based on its predictions, evaluations of its potential and capabilities, subsequent protein modeling methods from other groups and from Deepmind itself, etc. Here’s a small guide to my articles at TDS Editors.

A quick search for “alphafold” on Google Trends shows a small peak by late 2018 when the AlphaFold 1 was presented in CASP13; and then a high peak in late 2020 when AlphaFold 2 was presented in CASP14 followed by a lower but broader peak when the paper and code were released in July 2021. Clearly, this last peak hasn’t yet dropped to zero even after three months, and this is due to the various sources of new material on AlphaFold, essentially in the form of Twitter discussions and research preprints, some already peer-reviewed papers.

Search for “alphafold” on Google according to Google Trends. Figure by author Luciano Abriata.

My articles in TDS Editors

To keep readers up to date with the fast-evolving literature, I have written these articles in Towards Data Science:

  • My first article presents the breakthrough that allows everybody to run AlphaFold 2 at no cost using resources provided freely by Google: AlphaFold 2 available as Colab notebooks.
  • My next blog article presents new advances of the Colab notebooks, and the paper where Deepmind and the European Bioinformatics Institute present their project to model the structures of all natural proteins:



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