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Timothy Fei Truong Jr

2 accepted papers

2025

Understanding protein function with a multimodal retrieval-augmented foundation model

NeurIPS 2025poster

Protein language models (PLMs) learn probability distributions over natural protein sequences. By learning from hundreds of millions of natural protein sequences, protein understanding and design capabilities emerge. Recent works have shown that scaling these models improves structure prediction, bu…

Cited by 0SourceScholar
2023

PoET: A generative model of protein families as sequences-of-sequences

NeurIPS 2023poster

Generative protein language models are a natural way to design new proteins with desired functions. However, current models are either difficult to direct to produce a protein from a specific family of interest, or must be trained on a large multiple sequence alignment (MSA) from the specific family…