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The archive contains protein embeddings for 214M million proteins from AFDB v3. Protein data are divided into 25,339 batches of approximately 10,000 proteins in each. All data are stored in pickle format and can be opened as pandas Data Frame. To find a specific protein, use batch-to-protein-index.csv, which lists the contents of each batch.


The embedding is based on the protein's tertiary structure and can be used in search engines and similarity search applications. We benchmarked this embedding, assessing the correlation between protein structural information and embedding similarity. The metric used to assess structural similarity was a structure alignment method TM-Align; the embedding distance was computed as Euclidean (or Cosine) distance between the vectors. These results are available at https://github.com/Coda-Research-Group/ProteinEmbeddingBenchmark . The repository also contains resources needed to encode arbitrary protein into this embedding."},"titles":[{"title":{"en":"LMI-30: Protein structural embeddings of AlphaFold DB"},"titleType":"mainTitle"},{"title":{"en":"214 millions of proteins encoded into a vector representation (size=435)"},"titleType":"subtitle"}]},"revision":486,"updated":"2026-04-27T08:55:48.201231+00:00"}