29 citations · 40 across the 4 of their papers we have counts for
4 papers
Understanding protein function with a multimodal retrieval-augmented foundation model
Timothy Fei Truong, Tristan Bepler
Protein language models (PLMs) learn probability distributions over natural protein sequences. By learning from hundreds of millions of natural protein sequences, protein understan…
PoET: A generative model of protein families as sequences-of-sequences
Timothy F. Truong, Tristan Bepler
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…
Antibody Representation Learning for Drug Discovery
Lin Li, Esther Gupta, John Spaeth +3
Therapeutic antibody development has become an increasingly popular approach for drug development. To date, antibody therapeutics are largely developed using large scale experiment…
Few Shot Protein Generation
Soumya Ram, Tristan Bepler
We present the MSA-to-protein transformer, a generative model of protein sequences conditioned on protein families represented by multiple sequence alignments (MSAs). Unlike existi…