Jinyuan Sun

Protenix-v2 improves structure prediction and biomolecular design, achieving higher success rates in antibody-antigen and GPCR target tasks, with high developability and diversity in resulting hits.

PymolFold integrates SOTA protein structure predictors into PyMOL, enabling users to predict, visualize, and assess protein structures with minimal technical barriers.

PXDesign achieves high hit rates for protein binder design by combining generative models and filtering strategies, validated experimentally and made publicly available.

Pythia-PPI improves prediction of protein-protein binding affinity changes by using multi-task learning and self-distillation, achieving higher accuracy than existing methods.

The paper uses a language model to design cis-regulatory elements for prokaryotes without experimental data, achieving high success rates in gene expression and promoter activity.

This paper introduces Pythia, a self-supervised graph neural network for fast and accurate prediction of mutation-induced stability changes across protein structures, showing superior performance and speed.