Zhaokun Wang

This study investigates the assembly and conformational dynamics of insect odorant receptor complexes using high-speed atomic force microscopy and cryo-EM, revealing non-tetrameric oligomeric states and symmetry-broken configurations.

PBCNet2.0 predicts protein-ligand binding affinity using a cartesian tensor-based Siamese Neural Network, achieving high accuracy and efficiency compared to physics-based simulations.

SurfDock improves protein-ligand complex prediction by integrating multiple protein representations into a diffusion model, achieving higher accuracy and physical plausibility than existing methods.

PertKGE improves compound-protein interaction prediction using knowledge graph embedding of perturbation transcriptomics, outperforming baselines in cold-start settings and identifying novel therapeutic targets.