Simulating 500 million years of evolution with a language model

By Thomas Hayes, Roshan Rao, Halil Akin, Nicholas J. Sofroniew, Deniz Oktay, Zeming Lin, Robert Verkuil, Vincent Q. Tran, Jonathan Deaton, Marius Wiggert and 15 more. bioRxiv, 2024

677 citations recorded by the source corpus.

More than three billion years of evolution have produced an image of biology encoded into the space of natural proteins. Here we show that language models trained on tokens generated by evolution can act as evolutionary simulators to generate functional proteins that are far away from known proteins. We present ESM3, a frontier multimodal generative language model that reasons over the sequence, structure, and function of proteins. ESM3 can follow complex prompts combining its modalities and is highly responsive to biological alignment. We have prompted ESM3 to generate fluorescent proteins with a chain of thought. Among the generations that we synthesized, we found a bright fluorescent protein at far distance (58% identity) from known fluorescent proteins. Similarly distant natural fluorescent proteins are separated by over five hundred million years of evolution.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/624e12186c35b482e373466df30a046bb0a3ad60

Citations in the corpus

• Gaia: An AI-enabled genomic context–aware platform for protein sequence annotation (2025, unknown): “Models like ESM2 ( 10 ) and ESM3 ( 11 ) have shown promise in capturing complex patterns in protein sequences, enabling more sensitive remote homology detection.”