Chengyue Gong

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.

Protenix-v1 is an open-source biomolecular structure prediction model that outperforms AlphaFold3 with the same training data cutoff, offering improved accuracy and new features for real-world applications.

This paper introduces SLEEC, a semi-supervised framework for enzyme classification that achieves state-of-the-art performance and provides residue-level annotations.

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

An open AlphaFold3 reproduction, with weights, inference and training code, that matches AF3 on ligand and RNA benchmarks, beats AlphaFold-Multimer 2.3 on protein interfaces and flags possible memorisation.

The paper introduces ISM, a sequence-only model that improves upon ESM2 by incorporating structural information, achieving better performance on mutation stability and structure prediction tasks.