Yufei Huang

According to our database, Yufei Huang authored at least 34 papers between 2022 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Homophily-Enhanced Self-Supervision for Graph Structure Learning: Insights and Directions.

Lirong Wu, Haitao Lin, Zihan Liu, Zicheng Liu, Yufei Huang, Stan Z. Li

IEEE Trans. Neural Networks Learn. Syst., September, 2024

Teach Harder, Learn Poorer: Rethinking Hard Sample Distillation for GNN-to-MLP Knowledge Distillation.

Lirong Wu, Yunfan Liu, Haitao Lin, Yufei Huang, Stan Z. Li

CoRR, 2024

CBGBench: Fill in the Blank of Protein-Molecule Complex Binding Graph.

Haitao Lin, Guojiang Zhao, Odin Zhang, Yufei Huang, Lirong Wu, Zicheng Liu, Siyuan Li, Cheng Tan, Zhifeng Gao, Stan Z. Li

CoRR, 2024

GenBench: A Benchmarking Suite for Systematic Evaluation of Genomic Foundation Models.

Zicheng Liu, Jiahui Li, Siyuan Li, Zelin Zang, Cheng Tan, Yufei Huang, Yajing Bai, Stan Z. Li

CoRR, 2024

UniIF: Unified Molecule Inverse Folding.

Zhangyang Gao, Jue Wang, Cheng Tan, Lirong Wu, Yufei Huang, Siyuan Li, Zhirui Ye, Stan Z. Li

CoRR, 2024

Learning to Predict Mutation Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning.

Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li

CoRR, 2024

Deep Lead Optimization: Leveraging Generative AI for Structural Modification.

Odin Zhang, Haitao Lin, Hui Zhang, Huifeng Zhao, Yufei Huang, Yuansheng Huang, Dejun Jiang, Chang-Yu Hsieh, Peichen Pan, Tingjun Hou

CoRR, 2024

Deep Geometry Handling and Fragment-wise Molecular 3D Graph Generation.

Odin Zhang, Yufei Huang, Shichen Cheng, Mengyao Yu, Xujun Zhang, Haitao Lin, Yundian Zeng, Mingyang Wang, Zhenxing Wu, Huifeng Zhao, Zaixi Zhang, Chenqing Hua, Yu Kang, Sunliang Cui, Peichen Pan, Chang-Yu Hsieh, Tingjun Hou

CoRR, 2024

FoldToken: Learning Protein Language via Vector Quantization and Beyond.

Zhangyang Gao, Cheng Tan, Jue Wang, Yufei Huang, Lirong Wu, Stan Z. Li

CoRR, 2024

Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directions.

Rui Sun, Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li

CoRR, 2024

Learning to Predict Mutational Effects of Protein-Protein Interactions by Microenvironment-aware Hierarchical Prompt Learning.

Lirong Wu, Yijun Tian, Haitao Lin, Yufei Huang, Siyuan Li, Nitesh V. Chawla, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

PPFLOW: Target-Aware Peptide Design with Torsional Flow Matching.

Haitao Lin, Odin Zhang, Huifeng Zhao, Dejun Jiang, Lirong Wu, Zicheng Liu, Yufei Huang, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

GeoAB: Towards Realistic Antibody Design and Reliable Affinity Maturation.

Haitao Lin, Lirong Wu, Yufei Huang, Yunfan Liu, Odin Zhang, Yuanqing Zhou, Rui Sun, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

VQDNA: Unleashing the Power of Vector Quantization for Multi-Species Genomic Sequence Modeling.

Siyuan Li, Zedong Wang, Zicheng Liu, Di Wu, Cheng Tan, Jiangbin Zheng, Yufei Huang, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Re-Dock: Towards Flexible and Realistic Molecular Docking with Diffusion Bridge.

Yufei Huang, Odin Zhang, Lirong Wu, Cheng Tan, Haitao Lin, Zhangyang Gao, Siyuan Li, Stan Z. Li

Proceedings of the Forty-first International Conference on Machine Learning, 2024

MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding.

Lirong Wu, Yijun Tian, Yufei Huang, Siyuan Li, Haitao Lin, Nitesh V. Chawla, Stan Z. Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training Tasks.

Tianyu Fan, Lirong Wu, Yufei Huang, Haitao Lin, Cheng Tan, Zhangyang Gao, Stan Z. Li

Proceedings of the Twelfth International Conference on Learning Representations, 2024

PSC-CPI: Multi-Scale Protein Sequence-Structure Contrasting for Efficient and Generalizable Compound-Protein Interaction Prediction.

Lirong Wu, Yufei Huang, Cheng Tan, Zhangyang Gao, Bozhen Hu, Haitao Lin, Zicheng Liu, Stan Z. Li

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Protein 3D Graph Structure Learning for Robust Structure-Based Protein Property Prediction.

Yufei Huang, Siyuan Li, Lirong Wu, Jin Su, Haitao Lin, Odin Zhang, Zihan Liu, Zhangyang Gao, Jiangbin Zheng, Stan Z. Li

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

MMDesign: Multi-Modality Transfer Learning for Generative Protein Design.

Jiangbin Zheng, Siyuan Li, Yufei Huang, Zhangyang Gao, Cheng Tan, Bozhen Hu, Jun Xia, Ge Wang, Stan Z. Li

CoRR, 2023

Protein 3D Graph Structure Learning for Robust Structure-based Protein Property Prediction.

Yufei Huang, Siyuan Li, Jin Su, Lirong Wu, Odin Zhang, Haitao Lin, Jingqi Qi, Zihan Liu, Zhangyang Gao, Yuyang Liu, Jiangbin Zheng, Stan Z. Li

CoRR, 2023

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration.

Haitao Lin, Yufei Huang, Haotian Zhang, Lirong Wu, Siyuan Li, Zhiyuan Chen, Stan Z. Li

CoRR, 2023

Lightweight Contrastive Protein Structure-Sequence Transformation.

Jiangbin Zheng, Ge Wang, Yufei Huang, Bozhen Hu, Siyuan Li, Cheng Tan, Xinwen Fan, Stan Z. Li

CoRR, 2023

Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy.

Yufei Huang, Lirong Wu, Haitao Lin, Jiangbin Zheng, Ge Wang, Stan Z. Li

CoRR, 2023

A Survey on Protein Representation Learning: Retrospect and Prospect.

Lirong Wu, Yufei Huang, Haitao Lin, Stan Z. Li

CoRR, 2023

Functional-Group-Based Diffusion for Pocket-Specific Molecule Generation and Elaboration.

Haitao Lin, Yufei Huang, Odin Zhang, Yunfan Liu, Lirong Wu, Siyuan Li, Zhiyuan Chen, Stan Z. Li

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs.

Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li

Proceedings of the International Conference on Machine Learning, 2023

Extracting Low-/High- Frequency Knowledge from Graph Neural Networks and Injecting It into MLPs: An Effective GNN-to-MLP Distillation Framework.

Lirong Wu, Haitao Lin, Yufei Huang, Tianyu Fan, Stan Z. Li

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Non-equispaced Fourier Neural Solvers for PDEs.

Haitao Lin, Lirong Wu, Yongjie Xu, Yufei Huang, Siyuan Li, Guojiang Zhao, Stan Z. Li

CoRR, 2022

Protein Language Models and Structure Prediction: Connection and Progression.

Bozhen Hu, Jun Xia, Jiangbin Zheng, Cheng Tan, Yufei Huang, Yongjie Xu, Stan Z. Li

CoRR, 2022

DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding.

Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, Stan Z. Li

CoRR, 2022

Automated Graph Self-supervised Learning via Multi-teacher Knowledge Distillation.

Lirong Wu, Yufei Huang, Haitao Lin, Zicheng Liu, Tianyu Fan, Stan Z. Li

CoRR, 2022

Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks.

Lirong Wu, Haitao Lin, Yufei Huang, Stan Z. Li

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Using Context-to-Vector with Graph Retrofitting to Improve Word Embeddings.

Jiangbin Zheng, Yile Wang, Ge Wang, Jun Xia, Yufei Huang, Guojiang Zhao, Yue Zhang, Stan Z. Li

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2022