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.
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.
Developing AI methods to generate novel anticancer peptides while jointly considering activity, selectivity, safety, and developability.