RITA: a Study on Scaling Up Generative Protein Sequence Models


In this work we introduce RITA: a suite of autoregressive generative models for protein sequences, with up to 1.2 billion parameters, trained on over 280 million protein sequences belonging to the UniRef-100 database. Such generative models hold the promise of greatly accelerating protein design. We conduct the first systematic study of how capabilities evolve with model size for autoregressive transformers in the protein domain: we evaluate RITA models in next amino acid prediction, zero-shot fitness, and enzyme function prediction, showing benefits from increased scale. We release the RITA models openly, to the benefit of the research community.

International Conference on Machine Learning, Workshop on Computational Biology, 2022
Pascal Notin
Pascal Notin
PhD Candidate in Computer Science

Research in ML for computational biology and chemistry