Biography

I am a Research Associate at the City University of New York (CUNY) and Weill Cornell Medicine, Cornell University, working with Prof. Lei Xie.

I earned my Ph.D. in Computer Science from the Graduate Center, CUNY, under the supervision of Prof. Lei Xie. Prior to that, I obtained my B.S. in Physics from the University of Science and Technology of China (USTC), where I conducted research under the guidance of Prof. Zhenyu Li and Prof. Wenhua Zhang. I also worked on computational materials science at the University of Maryland, College Park (UMD).

In addition to my academic research, I gained industry experience as a Machine Learning Engineer Intern at Pinterest and as a Drug Discovery Research Scientist Intern at ByteDance, where I was supervised by Prof. Lei Li and Prof. Hao Zhou.

My research focuses on artificial intelligence for scientific discovery (AI for Science), with an emphasis on applications in drug discovery and molecular sciences. I’m open to collaborations on interesting projects.

News

Publications

* indicates equal contribution; † indicates corresponding author.

Sequence-based Drug-Target Binding Site Pre-training Enables Cryptic Pocket Detection and Improves Binding Affinity and Kinetics Prediction
Shuo Zhang, Li Xie, Daniel Tiourine, Lei Xie

Journal of Cheminformatics, 2026 (5-year Impact Factor: 8.9).

Protein Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence
Shuo Zhang, Lei Xie

Neural Information Processing Systems (NeurIPS) AI for Science Workshop, 2023.

Multimodal Out-of-Distribution Individual Uncertainty Quantification Enhances Binding Affinity Prediction for Polypharmacology
Amitesh Badkul, Li Xie, Shuo Zhang, Lei Xie

Nature Machine Intelligence, 2025 (5-year Impact Factor: 31.8).

                

TrustAffinity: Accurate, Reliable, and Scalable Out-of-Distribution Protein–Ligand Binding Affinity Prediction Using Trustworthy Deep Learning
Amitesh Badkul, Li Xie, Shuo Zhang, Lei Xie

Neural Information Processing Systems (NeurIPS) New Frontiers of AI for Drug Discovery and Development (AI4D3) Workshop, 2023.

Learning Point Cloud Representations with Pose Continuity for Depth-Based Category-Level 6D Object Pose Estimation
Zhujun Li, Shuo Zhang, Ioannis Stamos

International Conference on Computer Vision (ICCV) Recovering 6D Object Pose (R6D) Workshop, 2025.

MolGene-E: Inverse Molecular Design to Modulate Single Cell Transcriptomics
Rahul Ohlan, Raswanth Murugan, Li Xie, Mohammadsadeq Mottaqi, Shuo Zhang†, Lei Xie

International Conference on Machine Learning (ICML) AI4Science Workshop, 2024.

A Universal Framework for Accurate and Efficient Geometric Deep Learning of Molecular Systems
Shuo Zhang, Yang Liu, Lei Xie

Scientific Reports, 2023.

    

Physics-aware Graph Neural Network for Accurate RNA 3D Structure Prediction
Shuo Zhang, Yang Liu, Lei Xie

Neural Information Processing Systems (NeurIPS) Machine Learning for Structural Biology (MLSB) Workshop, 2022.

Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures
Shuo Zhang, Yang Liu, Lei Xie

Neural Information Processing Systems (NeurIPS) Machine Learning for Structural Biology (MLSB) Workshop, 2020.

    

End-to-End Sequence-Structure-Function Meta-Learning Predicts Genome-Wide Chemical-Protein Interactions for Dark Proteins
Tian Cai, Li Xie, Shuo Zhang, Muge Chen, Di He, Amitesh Badkul, Yang Liu, Hari Krishna Namballa, Michael Dorogan, Wayne W. Harding, Cameron Mura, Philip E. Bourne, Lei Xie

PLOS Computational Biology, 2023.

Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation
Shuo Zhang, Lei Xie

International Joint Conference on Artificial Intelligence (IJCAI), 2020.

Acceptance rate: 592/4717=12.6%.

Enhancing Attention-based Graph Neural Networks via Cardinality Preservation
Shuo Zhang, Lei Xie

AAAI Conference on Artificial Intelligence (AAAI) Deep Learning on Graphs (DLG) Workshop, 2020.

Heterogeneous Multi-Layered Network Model for Omics Data Integration and Analysis
Bohyun Lee, Shuo Zhang, Aleksandar Poleksic, Lei Xie

Frontiers in Genetics, 2020.

Theoretical Study of Adsorption and Dehydrogenation of C2H4 on Cu(410)
Yangyunli Sun*, Shuo Zhang*, Wenhua Zhang, Zhenyu Li

Chinese Journal of Chemical Physics, 2018.

Research Word Cloud

Services

• Conference Reviewer:
  • Neural Information Processing Systems (NeurIPS) (2021 - 2026)
  • International Conference on Machine Learning (ICML) (2021 - 2025)
  • International Conference on Learning Representations (ICLR) (2022 - 2026)
  • International Conference on Computer Vision (ICCV) (2025)
  • International Conference on Machine Learning & Applications (CMLA) (2020)
  • Machine Learning in Structural Biology (MLSB) Workshop (2023, 2025)
• Journal Reviewer:
  • Nature Communications
  • Journal of Cheminformatics
  • Bioinformatics
  • Journal of Chemical Information and Modeling
  • PLOS Computational Biology
  • BMC Bioinformatics
  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • Computer Vision and Image Understanding
  • International Journal of Advanced Computer Science and Applications

Others

• Patent:
  • Method and Apparatus for Designing Ligand Molecules. Yuwei Yang, Jiarui Lu, Shuo Zhang, Hao Zhou. US Patent App. 18/839,008, 2025.
• Presentation:
  • Cell-Type Specific Molecular Design for Next-Generation Polypharmacology. Appel Poster Event and Symposium, May. 2025.
  • Keynote: Accurate High-throughput Cryptic Binding Site Prediction Using Protein Language Model. International Conference on Intelligent Systems for Molecular Biology (ISMB), Jul. 2024.
  • Scalable and Accurate Target-Based Compound Screening Using Large Language Model. Appel Poster Event and Symposium, May. 2024.
  • Protein Large Language Model-Powered 3D Ligand Binding Site Prediction from Protein Sequence. LLMs4Bio Workshop at AAAI Conference on Artificial Intelligence (AAAI), Feb. 2024.
  • Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation. International Joint Conference on Artificial Intelligence (IJCAI), Jan. 2020
  • Enhancing Attention-based Graph Neural Networks via Cardinality Preservation. Deep Learning on Graphs Workshop at AAAI Conference on Artificial Intelligence (AAAI), Dec. 2019.
• Hobby: I enjoy reading, traveling, and exploring Hi-Fi audio.