From Supervision to Exploration: What Does Protein Language Model Learn During Reinforcement Learning?
   Hanqun Cao*, Hongrui Zhang*, Junde Xu*, Zhou Zhang*, Lingdong Shen, Minghao Sun, Ge Liu, Jinbo Xu, Wu-Jun Li, Jinren Ni, Cesar de la Fuente-Nunez, Tianfan Fu, Yejin Choi, Pheng-Ann Heng, Fang Wu^†
   Under Review
   [Paper]

   A Deep Reinforcement Learning Platform for Antibiotic Discovery.
   Hanqun Cao, Marcelo D. T. Torres, Jingjie Zhang, Zijun Gao, Fang Wu, Chunbin Gu, Jure Leskovec, Yejin Choi, Cesar de la Fuente-Nunez, Guangyong Chen, Pheng-Ann Heng^†
   Under Review
   [Paper]    [Code]

   Joint Design of Protein Surface and Structure Using a Diffusion Bridge Model.
   Guanlue Li, Xufeng Zhao, Fang Wu,^† , Soren Laue^†
   NeurIPS 2025
   [Paper]    [Code]

   D-Flow: Multi-modality Flow Matching for D-peptide Design.
   Fang Wu*, Tinson Xu*, Shuting Jin*, Xiangru Tang, Zerui Xu, James Zou, Brian Hie^†
   NeurIPS 2025 AI4D3 Workshop
   [Paper]    [Code]

   SurfDesign: Effective Protein Design on Molecular Surfaces.
   Fang Wu, Shuting Jin, Jianmin Wang, Zerui Xu, xiangxiang Zeng, Jinbo Xu^†
   Under review
   [Paper]    [Code]

   BC-Design: A Biochemistry-Aware Framework for High-Precision Inverse Protein Folding.
   Xiangru Tang*, Xinwu Ye*, Fang Wu*, Yanjun Shao, Yin Fang, Siming Chen, Dong Xu, Mark Gerstein^†
   ICML 2025 GenBio Workshop
   [Paper]    [Code]

   Surface-based Peptide Design with Multi-modal Flow Matching.
   Fang Wu*, Shuting Jin, Zhengyuan Zhou, Xiangxiang Zeng, Jure Leskovec, Jinbo Xu^†
   KDD 2025
   [Paper]

   A Survey of Generative AI for de novo Drug Design: New Frontiers in Molecule and Protein Generation.
   Xiangru Tang*, Howard Dai*, Elizabeth Knight*, Fang Wu,, Yunyang Li, Tianxiao Li, Mark Gerstein^†
   Briefings in Bioinformatics (2024)
   [Paper]    [Github Repo.]

   A Hierarchical Training Paradigm for Antibody Structure-sequence Co-design
   Fang Wu, Stan Z. Li^†
   NeurIPS 2023
   [Paper]

   PoseX: AI Defeats Physics Approaches on Protein-Ligand Cross Docking.
   Yize Jiang*, Xinze Li*, Yuanyuan Zhang*, Jin Han*, Youjun Xu*, Ayush Pandit, Zaixi Zhang, Mengdi Wang, Mengyang Wang, Chong Liu, Guang Yang, Yejin Choi, WuJun Li^†, Tianfan Fu^†, Fang Wu^†, Junhong Liu^†
   ICLR 2026
   [Paper]    [Code]    [Webpage]

   StaB-ddG: Predicting mutational effects on protein binding from folding energy.
   Arthur Deng, Karsten D. Householder, Fang Wu, Sebastian Thrun, K. Christopher Garcia, Brian L. Trippe^†
   ICML 2025
   [Paper]    [Code]

   Dynamics-inspired Structure Hallucination for Protein-protein Interaction Modeling.
   Fang Wu, Stan Z. Li^†
   TMLR (2025) → ICLR 2026 (Journal-to-Conference Track)
   [Paper]    [Code]

   Interformer: An Interaction-Aware Model for Protein-Ligand Docking and Affinity Prediction.
   Houtim Lai^†, Longyue Wang^†, Ruiyuan Qian, Juhong Huang, Peng Zhou, Geyan Ye, Fandi Wu, Fang Wu, Xiangxiang Zeng, Wei Liu
   Nature Communications (2024)
   [Paper]    [Code]

   Surface-VQMAE: Vector-quantized Masked Auto-encoders on Molecular Surfaces.
   Fang Wu, Stan Z. Li^†
   ICML 2024
   [Paper]    [Code]

   Integration of Pre-trained Protein Language Models into Geometric Deep Learning Networks.
   Fang Wu, Liong Wu, Dragomir Radev, Jinbo Xu, Stan Z. Li^†
   Communications Biology (2023)
   [Paper]    [Code]

   Molecular Representations in Implicit Functional Space via Hyper-Networks.
   Zehong Wang, Xiaolong Han, Qi Yang, Xiangru Tang, Fang Wu, Xiaoguang Guo, Weixiang Sun, Tianyi Ma, Pietro Lio, Le Cong, Sheng Wang, Chuxu Zhang, Yanfang Ye^†
   Under Review
   [Paper]    [Code]

   Instructor-inspired Machine Learning for Robust Molecular Property Prediction.
   Fang Wu*^†, Shuting Jin*, Siyuan Li, Stan Z. Li
   NeurIPS 2024
   [Paper]    [Code]

   A Semi-supervised Molecular Learning Framework for Activity Cliff Estimation.
   Fang Wu^†
   IJCAI 2024
   [Paper]    [Code]

   Improving Molecular Representation Learning with Metric Learning-enhanced Optimal Transport
   Fang Wu*, Nicolas Courty*, Shuting Jin*, Stan Z. Li^†
   Patterns (2023)
   [Paper]    [Code]

   Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs.
   Fang Wu, Dragomir Radev, Stan Z. Li^†
   AAAI 2023 (oral)
   [Paper]    [Code]

   Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling.
   Fang Wu, Bozhen Hu, Stan Z. Li^†
   AAAI 2025
   [Paper]

   DiffMD: A Geometric Diffusion Model for Molecular Dynamics Simulations
   Fang Wu, Stan Z. Li^†
   AAAI 2023 (Oral)
   [Paper]

   Pretraining of Equivariant Graph Matching Networks with Conformation Flexibility for Drug Binding
   Fang Wu*, Shuting Jin*, Yinghui Jiang*, Xurui Jin, Bowen Tang, Zhangming Niu, Qiang Zhang, Xiangxiang Zeng, Stan Z. Li^†
   Advanced Science (2022)
   [Paper]    [Code]

   L2M3OF: A Large Language Multimodal Model for Metal-Organic Frameworks.
   Jiyu Cui*, Fang Wu*, Haokai Zhao*, Minggao Feng, Xenophon Evangelopoulos, Andrew I. Cooper^†, Yejin Choi^†
   Under Review
   [Paper]    [Code]

   Direct Prediction of Gas Adsorption via Spatial Atom Interaction Learning.
   Jiyu Cui*, Fang Wu*, Wen Zhang*, Lifeng Yang*, Jianbo Hu, Yin Fang, Peng Ye, Qiang Zhang, Xian Suo, Yiming Mo, Xili Cui, Huajun Chen^†, Huabin Xing^†
   Nature Communications (2023)
   [Paper]    [Code]

   InsertGNN: A Hierarchical Graph Neural Network for the TOEFL Sentence Insertion Problem
   Fang Wu^†, Stan Z. Li
   EMNLP 2024 Findings
   [Paper]    [Data]

   Discovering the Representation Bottleneck of Graph Neural Networks
   Fang Wu*, Siyuan Li*, Dragomir Radev, Stan Z. Li^†
   IEEE TKDE (2024)
   [Paper]    [Code]

   Rethinking Explaining Graph Neural Networks via Non-parametric Subgraph Matching
   Fang Wu, Siyuan Li, Dragomir Radev, Stan Z. Li^†
   ICML 2023
   [Paper]    [Code]