John Z. H. Zhang

CatESO optimizes enzyme sequences to improve catalytic turnover by integrating kcat prediction with differentiable design, achieving higher predicted activity and structural confidence than existing methods.

This study investigates how the small-molecule agonist VG-3927 stabilizes the TREM2-DAP12 complex by binding within a transmembrane cavity and altering its conformation.

This study investigates the short-T1 fraction as a biomarker of myelin content using postmortem MRI and histology, showing strong correlations with myelin water imaging and histological markers.

The paper designs a protein that binds to multiple SARS-CoV-2 RBD variants with high affinity, demonstrating a strategy for broad-spectrum antiviral inhibitors.

SAKE-PP improves PPI model evaluation by predicting interface RMSD without native references, outperforming AF3 on docking decoys and antibody-antigen complexes.

The paper develops two GPCR-specific models, DeepGPCR_BC and DeepGPCR_RG, for binary classification and affinity prediction of ligand interactions, achieving better performance than existing tools.

ChemXTree improves small-molecule drug discovery by integrating tree-based algorithms with a graph model, showing superior performance in feature optimization on benchmark datasets.

This paper introduces DeepBindGCN, a model that predicts protein-ligand interactions using graph convolutional networks and molecular vectors, achieving high accuracy in benchmark datasets.

This paper generates and screens bioactive peptides using deep learning models, demonstrating their potential for therapeutic applications.

The paper generates and screens a bioactive peptide against Xanthine oxidase using LSTM_Pep and DeepPep, achieving a 64.32% inhibitory activity with an IC50 of 3.76mg/mL.