Tianfeng Shang

HighPlay：Cyclic Peptide Sequence Design Based on Reinforcement Learning and Protein Structure Prediction

HighPlay uses reinforcement learning and protein structure prediction to design cyclic peptides with good binding affinity, validated through molecular dynamics simulation and experimental testing.

NCPepFold: Accurate Prediction of Non-canonical Cyclic Peptide Structures via Cyclization Optimization with Multigranular Representation

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

Predicting the structures of cyclic peptides containing unnatural amino acids by HighFold2

HighFold2 predicts 3D structures of cyclic peptides with unnatural amino acids, achieving a median RMSD of 1.891 Å for monomers and complexes.

MeDCycFold: A Rosetta Distillation Model to Accelerate Structure Prediction of Cyclic Peptides with Backbone N-methylation and D-amino Acids

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

GAPS: Geometric Attention-based Networks for Peptide Binding Sites Identification by the Transfer Learning Approach

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

Highfold: accurately predicting cyclic peptide monomers and complexes with AlphaFold

Highfold predicts cyclic peptides and their complexes using AlphaFold, improving accuracy by incorporating structural information.

Multi_CycGT: a DL-based multimodal model for membrane permeability prediction of cyclic peptides

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