Runze Zhang

The paper introduces ConfSeq, a conformation description language for 3D molecular structures, enabling sequence-based modeling and achieving state-of-the-art results in 3D molecular tasks.

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.

PackDock predicts accurate protein-ligand complex conformations using a diffusion model for side-chain packing, validated through multiple docking experiments.

This study develops AOMP, a graph neural network for predicting AOX metabolism, outperforming benchmarks and identifying new scaffolds validated experimentally.

EquiScore is a protein-ligand interaction scoring method that outperforms existing methods on external test sets and shows potential for lead compound optimization.

The paper designs high-fidelity Cas13 variants with minimal collateral RNA degradation effects, validated in mammalian cells and transgenic mice.