Qichang Zhao

USP-ddG predicts mutational effects on protein-protein interactions by improving data efficiency and modeling conformational dynamics, outperforming existing methods on benchmark datasets.

PyrMol improves molecular property prediction by integrating expert knowledge into a hierarchical graph framework, outperforming existing methods on benchmark datasets.

TopoLa enhances cell representations in single-cell and spatial omics by using topology-encoded latent hyperbolic geometry, improving performance across multiple tasks.

This paper introduces a unified framework for drug-target interaction prediction using a semantic-guided meta-path method, achieving better performance and handling cold-start scenarios effectively.