Nathaniel Blalock

This study develops a machine learning framework to engineer enzymes by integrating sparse data with evolutionary and structural information, achieving improvements in catalytic performance, yield, and stability.

This paper introduces RLXF, a framework that aligns protein language models with experimental functional goals, improving the generation of high-functioning protein variants.

This paper uses machine learning to engineer olivetolic acid cyclase in yeast, enhancing cannabinoid production and selectivity.

PRISM improves antibody language modeling by distinguishing germline and non-germline residues, achieving better binding affinity correlation and controllable generation.