New ML framework for computational protein design that looks beyond natural sequences
Researchers describe a new machine‑learning framework intended to improve the success rate of computational protein design by generating solutions that do not merely reproduce sequences observed in nature. The approach is presented as a way to explore sequence space beyond natural examples while aiming to raise the proportion of designs that fold or function as intended.