EECS Special Seminar: Alex Rives, “Evolutionary scale language models"
Speaker
Alex Rives
Meta FAIR
Host
Caroline Uhler and Antonio Torralba
EECS / LIDS / CSAIL
Abstract:
Generative artificial intelligence has the potential to enable programmable biology. Advanced protein language models demonstrate emergent learning of atomic resolution structure and protein design principles. We study the scaling of language models across orders of magnitude in the number of parameters, and observe the development of an atomic level structure prediction capability. When applied generatively language models generalize beyond natural proteins. We have evaluated AI generated proteins in the laboratory with high success rates, including for proteins without significant sequence similarity to natural proteins. These developments enable programmable design of de novo protein sequences and structures of high complexity.
Bio:
Alex Rives is Scientific lead for the ESM protein language modeling team, and PhD candidate at NYU.
Generative artificial intelligence has the potential to enable programmable biology. Advanced protein language models demonstrate emergent learning of atomic resolution structure and protein design principles. We study the scaling of language models across orders of magnitude in the number of parameters, and observe the development of an atomic level structure prediction capability. When applied generatively language models generalize beyond natural proteins. We have evaluated AI generated proteins in the laboratory with high success rates, including for proteins without significant sequence similarity to natural proteins. These developments enable programmable design of de novo protein sequences and structures of high complexity.
Bio:
Alex Rives is Scientific lead for the ESM protein language modeling team, and PhD candidate at NYU.