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Excited to announce PoET, our (@timt1630, @tbepler1) retrieval-augmented generative protein language model that achieves state-of-the-art unsupervised variant function prediction performance on #ProteinGym. #MachineLearning #ProteinML 1/9
arxiv.org/abs/2306.06156
arxiv.org/abs/2306.06156
Inspired by evolution, PoET conditions on observed protein sequences to infer fitness constraints and extrapolate a generative distribution of protein sequences. This allows PoET to be focused on any level of homology, from superfamilies to families to subfamilies and beyond. 2/9
The key idea in the design of PoET was to create a transformer that could condition on homologous sequences but did not require aligned inputs. Our solution was to model the generative process of whole protein families as a sequence-of-sequences generative modeling problem. 3/9
