Qingyi Mao

High-PepBinder generates target-specific peptides using a pLM-guided diffusion model and a comprehensive dataset, producing peptides with favorable binding properties and sequence diversity.

This study evaluates AlphaFold3's accuracy in predicting non-canonical cyclic peptides and provides guidelines for its application.

HighFold3 improves cyclic peptide structure prediction, especially for those with unnatural amino acids, outperforming existing models.

NCPepFold predicts structures of non-canonical cyclic peptides with improved accuracy using multigranular representation and cyclization optimization.

MeDCycFold accelerates cyclic peptide structure prediction using a deep learning-based Rosetta distillation model, achieving 49x speedup while maintaining Rosetta-level accuracy.