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Status: Unchecked ×Keyword: retrieval-augmented generation ×Clear all

3 claims from 1 paper

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   RAG-ESM: Improving Pretrained Protein Language Models via Sequence Retrieval

   Sgarbossa and Bitbol · PRX Life · 2025

   The paper adds a retrieval-based method to pretrained ESM2 protein language models so they use homologous sequences, and reports gains in prediction, sequence generation and motif scaffolding.

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   1. UncheckedRAG-ESM models, which use homologous sequences as extra input, are reported to predict masked amino acids better than larger ESM2 models.“We show that RAG-ESM models outperform larger ESM2 models for masked amino acid prediction.”
   2. UncheckedIn RAG-ESM, certain cross-attention heads appear to learn to align protein sequences without being explicitly trained to do so.“We find that sequence alignment capabilities spontaneously emerge in specific cross-attention heads of RAG-ESM.”
   3. Unchecked“By using a discrete diffusion objective for training, and by conditioning on homologs during inference, RAG-ESM reaches state-of-the-art performance for conditional protein sequence generation and motif scaffolding, among sequence-based models.”