| Diffbp: Generative diffusion of 3d molecules for target protein binding H Lin, Y Huang, O Zhang, S Ma, M Liu, X Li, L Wu, J Wang, T Hou, SZ Li Chemical Science 16 (3), 1417-1431, 2025 | 78 | 2025 |
| Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge Y Huang, O Zhang, L Wu, C Tan, H Lin, Z Gao, S Li, S Li ICML 2024 (Spotlight), 2024 | 69* | 2024 |
| Knowledge distillation improves graph structure augmentation for graph neural networks L Wu, H Lin, Y Huang, SZ Li Advances in Neural Information Processing Systems 35, 11815-11827, 2022 | 56 | 2022 |
| Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction Y Huang, S Li, J Su, L Wu, O Zhang, H Lin, J Qi, Z Liu, Z Gao, Y Liu, ... AAAI 2024, 2023 | 43* | 2023 |
| Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs L Wu, H Lin, Y Huang, SZ Li ICML2023, 2023 | 33 | 2023 |
| Mape-ppi: Towards effective and efficient protein-protein interaction prediction via microenvironment-aware protein embedding L Wu, Y Tian, Y Huang, S Li, H Lin, NV Chawla, SZ Li arXiv preprint arXiv:2402.14391, 2024 | 31 | 2024 |
| Extracting Low-/High-Frequency Knowledge from Graph Neural Networks and Injecting it into MLPs: An Effective GNN-to-MLP Distillation Framework L Wu, H Lin, Y Huang, T Fan, SZ Li AAAI 2023, 2023 | 31 | 2023 |
| Homophily-Enhanced Self-supervision for Graph Structure Learning: Insights and Directions L Wu, H Lin, Y Huang, SZ Li IEEE TNNLS, 2023 | 26 | 2023 |
| Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings J Zheng, Y Wang, G Wang, J Xia, Y Huang, G Zhao, Y Zhang, SZ Li ACL 2022, 2022 | 25 | 2022 |
| Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration H Lin, Y Huang, H Zhang, L Wu, S Li, Z Chen, SZ Li Neurips 2023, 2023 | 19 | 2023 |
| DiffSDS: A geometric sequence diffusion model for protein backbone inpainting Z Gao, C Tan, L Wu, Y Huang, B Hu, SZ Li | 16* | |
| Data-efficient protein 3d geometric pretraining via refinement of diffused protein structure decoy Y Huang, L Wu, H Lin, J Zheng, G Wang, SZ Li arXiv preprint arXiv:2302.10888, 2023 | 12 | 2023 |
| Psc-cpi: Multi-scale protein sequence-structure contrasting for efficient and generalizable compound-protein interaction prediction L Wu, Y Huang, C Tan, Z Gao, B Hu, H Lin, Z Liu, SZ Li Proceedings of the AAAI Conference on Artificial Intelligence 38 (1), 310-319, 2024 | 10 | 2024 |
| Foldtoken: Learning protein language via vector quantization and beyond Z Gao, C Tan, J Wang, Y Huang, L Wu, SZ Li arXiv preprint arXiv:2403.09673, 2024 | 10 | 2024 |
| A survey on protein representation learning: Retrospect and prospect L Wu, Y Huang, H Lin, SZ Li arXiv preprint arXiv:2301.00813, 2022 | 9 | 2022 |
| Deep geometry handling and fragment-wise molecular 3d graph generation O Zhang, Y Huang, S Cheng, M Yu, X Zhang, H Lin, Y Zeng, M Wang, ... arXiv preprint arXiv:2404.00014, 2024 | 8 | 2024 |
| Learning complete protein representation by deep coupling of sequence and structure B Hu, C Tan, J Xia, J Zheng, Y Huang, L Wu, Y Liu, Y Xu, SZ Li bioRxiv, 2023.07. 05.547769, 2023 | 8 | 2023 |
| Non-equispaced fourier neural solvers for pdes H Lin, L Wu, Y Xu, Y Huang, S Li, G Zhao, SZ Li arXiv preprint arXiv:2212.04689, 2022 | 8 | 2022 |
| Learning to predict mutation effects of protein-protein interactions by microenvironment-aware hierarchical prompt learning L Wu, Y Tian, H Lin, Y Huang, S Li, NV Chawla, SZ Li arXiv preprint arXiv:2405.10348, 2024 | 7 | 2024 |
| Lightweight contrastive protein structure-sequence transformation J Zheng, G Wang, Y Huang, B Hu, S Li, C Tan, X Fan, SZ Li arXiv e-prints, arXiv: 2303.11783, 2023 | 7 | 2023 |
Stan Z. Li (李子青)Westlake University & CAS Institute of AutomationZweryfikowany adres z westlake.edu.cn