PoET-2 Introduces Retrieval-Augmented Multimodal Protein Foundation Model
On Feb 26, 2026, researchers published PoET-2, a multimodal, retrieval-augmented protein foundation model that integrates family-specific in-context evolutionary retrieval with optional structure conditioning. PoET-2 employs a hierarchical equivariant transformer encoder and dual decoders supporting generative and bidirectional modes, achieving state-of-the-art zero-shot variant effect prediction and improved supervised embeddings, notably for multi-mutation and indel scoring on small datasets.
Scoring Rationale
Strong methodological novelty and improved zero-shot performance, limited by single-source arXiv preprint lacking peer review.
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Sources
- Read Original[2508.04724] Understanding protein function with a multimodal retrieval-augmented foundation modelarxiv.org
