Author’s voice summary: The content provides a detailed visual walkthrough of how AlphaFold3 works, aimed at an ML audience. It explains the new features of the model, such as predicting structures of proteins complexed with other molecules, and the complex featurization scheme involved. The post delves into various sections of the architecture, from input preparation to structure prediction, highlighting the role of attention and Tensor representations. Unique aspects include the use of templates in model prediction and the Atom Transformer module. The diagrams simplify the model’s operations, focusing on how activations change. The inclusion of multiple sequence alignments and structures from templates adds depth to the structure prediction process, enhancing accuracy.
https://elanapearl.github.io/blog/2024/the-illustrated-alphafold/