Jian Zhang

This paper introduces a deep learning framework for identifying dynamic protein allosteric sites across different conformational states, achieving improved performance over existing methods.

This study fine-tunes an intracellular allosteric pocket of NTSR1 to program GPCR signaling bias, revealing how ligand modifications alter transducer coupling and downstream signaling.

ColBERT-PPI predicts protein partners and their binding sites at the residue level using a multi-vector framework, outperforming existing methods on benchmark datasets.

C-PLANK improves ligand-protein interaction prediction by integrating cellular context and fragment-based chemoproteomics data, outperforming existing methods and identifying novel ligands.

NeoPep designs functional peptides de novo using a deep-learning framework that models biophysical principles, achieving high hit rates and structural accuracy across diverse targets.

This paper introduces AnewOmni, a generative model that designs biomolecules across scales by learning interaction patterns and physical constraints from 5 million complexes.

The paper identifies non-covalent inhibitors of PRDX5, a protein upregulated in MPM, through a machine learning guided pipeline and biochemical validation, suggesting it as a therapeutic target.

This paper presents a quantum computing framework for drug discovery that improves stability and performance in multiple stages, identifying novel druggable sites and compounds.

The paper develops MutaPheno, a model that predicts cancer driver missense mutations using molecular features, achieving strong accuracy and outperforming existing tools.

PocketX uses reinforcement learning to align protein pocket design with biophysical properties, outperforming existing methods on binding energy and evolutionary plausibility.

MultiAlloDriver predicts and identifies cancer driver mutations by integrating protein sequence, structure, and surface information, achieving over 93% accuracy.

OriGene identifies therapeutic targets by integrating diverse data modalities and outperforms human experts and models in accuracy and robustness.

The paper uses lectin microarrays to analyze glycoproteins in salivary glands of Aedes aegypti and Anopheles stephensi, finding similar glycan profiles with specific oligomannose and O-glycan presence.

This study identifies SPP1+ tumor-associated macrophages as a key driver of radioresistance in nasopharyngeal carcinoma through interactions with the tumor microenvironment.

The paper examines how genetic variants in the cholinergic system affect theta band event-related oscillations, finding age and sex-dependent variations in these associations.

MOAHIT is a web tool that visualizes tumor multi-omics data with human anatomy heatmaps to explore tumor heterogeneity across different tissues and cancers.