Ziyao Li

UFConf improves protein conformation sampling using a diffusion model based on amino acid sequences, outperforming existing methods and showing utility in drug discovery.

Amalga improves protein backbone designability by using folding and inverse folding guidance to generate more designable structures.

Uni-Fold MuSSe predicts protein complex structures from primary sequences using pre-trained protein language models, showing potential for complex prediction.

UF-Symmetry improves prediction of large protein complexes by leveraging symmetry, achieving faster computation and better accuracy than existing methods.

PyTorch reimplementation of AlphaFold and AlphaFold-Multimer with training code, retrained from scratch to equal or better accuracy, about 2.2 times faster training and roughly 2% higher multimer TM-Score.