Silong Zhai

MAERM predicts enzyme-reaction matching relationships using a mixed-attention model, outperforming baselines and enabling enzyme design.

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.

PepInter is a deep learning framework that uses pre-trained language models to predict protein-peptide interactions and bioactivity, outperforming existing methods on benchmark datasets.

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

The paper introduces PepPCBench, a benchmark framework to evaluate AlphaFold3's performance in predicting protein-peptide complexes using a curated dataset.

GAPS uses geometric attention mechanisms and transfer learning to identify protein-peptide binding sites, achieving state-of-the-art performance.

This paper introduces Multi_CycGT, a deep learning model for predicting cyclic peptide membrane permeability, achieving high accuracy and AUC on a benchmark dataset.