Jiaqi Guan

Protenix-v2: Broadening the Reach of Structure Prediction and Biomolecular Design

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: Toward High-Accuracy Open-Source Biomolecular Structure Prediction

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.

Atom-level generative foundation model for molecular interaction with pockets

This paper introduces PocketXMol, a generative foundation model for molecular interactions with protein pockets, achieving state-of-the-art performance in 11 tasks and demonstrating success in designing inhibitors and peptides.

PXDesign: Fast, Modular, and Accurate De Novo Design of Protein Binders

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

Protenix - Advancing Structure Prediction Through a Comprehensive AlphaFold3 Reproduction

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.