Zheng, Shuangjia

DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model

Lu, W., Zhang, J.-X., Huang, W., Zhang, Z., Jia, X., Wang, Z., Shi, L., Li, C., Wolynes, P., & Zheng, S. (2023) · Nature Communications

Computational Drug Discovery MethodsProtein Structure and Dynamics

Cited by 243

AlphaFold2-aware protein–DNA binding site prediction using graph transformer

Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2022) · Briefings in Bioinformatics

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 137

AlphaFold2-aware protein-DNA binding site prediction using graph transformer

Yuan, Q., Chen, S., Rao, J., Zheng, S., Zhao, H., & Yang, Y. (2021) · Briefings in Bioinformatics

Machine Learning in BioinformaticsProtein Structure and Dynamics

Cited by 137

Quantitative evaluation of explainable graph neural networks for molecular property prediction

Rao, J., Zheng, S., Lu, Y., & Yang, Y. (2022) · Patterns

Explainable Artificial Intelligence (XAI)Computational Drug Discovery Methods

Cited by 90

A Systematic Comparison of Single-Cell Perturbation Response Prediction Models

Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory)

Gene Regulatory Network AnalysisSingle-cell and spatial transcriptomics

Cited by 25

From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery

Wei, J., Yang, Y., Zhang, X., Chen, Y., Zhuang, X., Gao, Z., Zhou, D., Wang, G., Gao, Z., Cao, J., et al. (2025) · arXiv

Artificial Intelligence ApplicationsMachine Learning in Materials Science

Cited by 7

A systematic comparison of single-cell perturbation response prediction models

Li, L., You, Y., Fu, Y., Liao, W., Fan, X., Lu, S., Cao, Y., Li, B., Ren, W., Kong, J., et al. (2026) · Science Advances

Single-cell and spatial transcriptomicsGene Regulatory Network Analysis

Cited by 1

Accurate Protein-Protein Interactions Modeling through Physics-informed Geometric Invariant Learning

Rao, J., Liu, D., Zhou, X., Yuan, Q., Wei, W., Lu, W., Zhang, J., Rong, Y., Yang, Y., & Zheng, S. (2025) · Communications Biology

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 1