Nikhil Naik

The paper fine-tunes language models on laboratory data to design antibodies with improved affinity, achieving sub-25 picomolar binding for four antibodies.

This paper shows that deep learning language models can generate functional artificial proteins across families, with activities similar to natural proteins and structures resembling natural folds.

A 1.2B-parameter autoregressive model trained on 280M tagged sequences completes held-out proteins with near-native Rosetta energies and ranks high-fitness GB1 variants without any GB1 training.