Classifying the stoichiometry of virus-like particles with interpretable machine learning
Interpretable machine learning for virus-like particle stoichiometry classification.
jiayang-zhang
Interpretable machine learning for virus-like particle stoichiometry classification.
Developing AI methods to generate novel anticancer peptides while jointly considering activity, selectivity, safety, and developability.
An agentic closed-loop laboratory that generates, screens, verifies, and iteratively improves selective and stable metal-binding proteins.
Studying protein AI across connected biological states through MuSProt, with MusBench for evaluating how models transfer knowledge and remain consistent between states.