Yufei Huang

Rider improves RNA virus discovery from metatranscriptomes by combining structure-informed screening with structural validation, outperforming existing tools and recovering divergent sequences.

ECloudGen improves molecular design by using electron clouds as latent variables, generating more potent binders and exploring broader chemical space than existing methods.

This paper introduces laruGL-PPI, a label-aware hierarchical subgraph learning method for protein-protein interaction prediction that improves performance and efficiency by addressing topological shortcuts and computational costs.

GeoAB improves antibody design by generating realistic CDR structures and enabling accurate affinity maturation through mutation effect prediction.

PPFlow designs target-aware peptides using torsional flow matching, achieving state-of-the-art performance in peptide drug generation and optimization.

This paper introduces CoupleNet, a model that integrates protein sequence and structure to improve representation learning, achieving better performance in fold classification and function prediction.