Jason Yang

EZSolver predicts polar enzymatic mechanisms without templates using flow matching and beam search, achieving 60% accuracy on unseen reactions.

This paper uses machine learning to guide the creation of enzyme libraries with broader catalytic activity, showing improved performance across multiple reactions.

This paper introduces Enzyme-toolkit, a framework for discovering new enzymes with specific activities, demonstrating its effectiveness in identifying enzymes for pollutant degradation.

This paper uses a generative AI model to design novel tryptophan synthase variants with enhanced catalytic activity and substrate promiscuity, outperforming natural and optimized versions.

This paper introduces ALDE, a machine learning-assisted directed evolution method that improves protein engineering outcomes by efficiently exploring epistatic mutation spaces.

PLAID generates controllable all-atom protein structures from sequences using a diffusion model trained on sequence data, enabling functional control through Gene Ontology and organism keywords.

This study evaluates machine learning-assisted directed evolution strategies across diverse protein fitness landscapes, finding that focused training with zero-shot predictors outperforms random sampling in challenging landscapes.

The paper constructs a large-scale sequence-fitness dataset for enzyme engineering, revealing significant epistasis and highlighting challenges in predicting optimal variants.