Research Interests

As AI advances at a breathtaking pace and we edge ever closer to AGI, I am deeply excited by the convergence of generative AI and agentic AI with the life science. My research is driven by the vision of enabling machines to understand the language of life and accelerate drug discovery, spanning the following areas:

• AI for Life Science and Drug Discovery Understanding small molecules, biomolecules, and their interactions, ultimately enabling their rational design and accelerating drug discovery.
• Generative AI Training and sampling paradigms for generative models in both continuous and discrete spaces, with their application to scientific problems.
• Agentic AI LLM-driven AI agents, spanning reasoning and agentic capabilities, harness engineering, recursive self-improvement (RSI), and their applications to scientific discovery.

Previously, I also worked on graph machine learning and data mining.

News

Selected Publications

(Full Publication List)

AI for Science

• Reading the Cell, Designing the Cure: Perturbation-Conditioned Molecular Diffusion for Function-Oriented Drug Design
  Ziyu Xu, Zijian Zhang, Liang Wang, Zhiyuan Liu, Qiang Liu, Shu Wu, Liang Wang
  ICML 2026
  [Paper] [Code]
• SpectraLLM: Uncovering the Ability of LLMs for Molecule Structure Elucidation from Multi-Spectral Data
  Yunyue Su, Jiahui Chen, Zao Jiang, Zhenyi Zhong, Liang Wang, Qiang Liu, Zhaoxiang Zhang
  ICLR 2026
  [Paper] [Code]
• EnzyControl: Adding Functional and Substrate-Specific Control for Enzyme Backbone Generation
  Chao Song, Zhiyuan Liu, Han Huang, Liang Wang, Qiong Wang, Jianyu Shi, Hui Yu, Yihang Zhou, Yang Zhang
  NeurIPS 2025
  [Paper] [Code]
• 3D-GSRD: 3D Molecular Graph Auto-Encoder with Selective Re-mask Decoding
  Chang Wu, Zhiyuan Liu, Wen Shu, Liang Wang, Yanchen Luo, Wenqiang Lei, Yatao Bian, Junfeng Fang, Xiang Wang
  NeurIPS 2025
  [Paper] [Code]
• MolSpectra: Pre-training 3D Molecular Representation with Multi-modal Energy Spectra
  Liang Wang, Shaozhen Liu, Yu Rong, Deli Zhao, Qiang Liu, Shu Wu, Liang Wang
  ICLR 2025
  [Paper] [Code] [Poster] [Slides] [Blog (Chinese)]
• Pin-Tuning: Parameter-Efficient In-Context Tuning for Few-Shot Molecular Property Prediction
  Liang Wang, Qiang Liu, Shaozhen Liu, Xin Sun, Shu Wu, Liang Wang
  NeurIPS 2024
  [Paper] [Code] [Poster]

Graph Machine Learning

• A Survey of Graph Transformers: Architectures, Theories and Applications
  Chaohao Yuan, Kangfei Zhao, Ercan Engin Kuruoglu, Liang Wang, Tingyang Xu, Wenbing Huang, Deli Zhao, Hong Cheng, Yu Rong
  ACM Computing Surveys, 2026
  [Paper]
• Rethinking Graph Masked Autoencoders through Alignment and Uniformity
  Liang Wang*, Xiang Tao*, Qiang Liu, Shu Wu, Liang Wang
  AAAI 2024
  [Paper] [Code] [Poster]
• DIVE: Subgraph Disagreement for Graph Out-of-Distribution Generalization
  Xin Sun, Liang Wang, Qiang Liu, Shu Wu, Zilei Wang, Liang Wang
  KDD 2024
  [Paper]
• GSLB: The Graph Structure Learning Benchmark
  Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu Yu
  NeurIPS 2023
  [Paper] [Code]

Data Mining

• S²DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completion
  Tengfei Ma, Yujie Chen, Liang Wang, Xuan Lin, Bosheng Song, Xiangxiang Zeng
  AAAI 2025
  [Paper]
• Bi-Level Graph Structure Learning for Next POI Recommendation
  Liang Wang, Shu Wu, Qiang Liu, Yanqiao Zhu, Xiang Tao, Mengdi Zhang, Liang Wang
  IEEE Transactions on Knowledge and Data Engineering 2024
  [Paper] [Code]
• Semantic Evolvement Enhanced Graph Autoencoder for Rumor Detection
  Xiang Tao, Liang Wang, Qiang Liu, Shu Wu, Liang Wang
  WWW 2024
  [Paper]

Preprints

• DiffSpectra: Molecular Structure Elucidation from Spectra using Diffusion Models
  Liang Wang, Yu Rong, Tingyang Xu, Zhenyi Zhong, Zhiyuan Liu, Pengju Wang, Deli Zhao, Qiang Liu, Shu Wu, Liang Wang, Yang Zhang
  arXiv preprint 2025
  [Paper] [Code] [Model Checkpoints] [Datasets]
• Diffusion Models for Molecules: A Survey of Methods and Tasks
  Liang Wang, Chao Song, Zhiyuan Liu, Yu Rong, Qiang Liu, Shu Wu, Liang Wang
  arXiv preprint 2025
  [Paper] [Awesome Paper List]
• Materials Generation in the Era of Artificial Intelligence: A Comprehensive Survey
  Zhixun Li*, Bin Cao*, Rui Jiao*, Liang Wang*, Ding Wang, Yang Liu, Dingshuo Chen, Jia Li, Qiang Liu, Yu Rong, Liang Wang, Tong-yi Zhang, Jeffrey Xu Yu
  arXiv preprint 2025
  [Paper] [Awesome Paper List]

Projects

Services

Conference Reviewer

• Conference on Neural Information Processing Systems (NeurIPS), 2024, 2025, 2026
• International Conference on Learning Representations (ICLR), 2025, 2026, 2027
• International Conference on Machine Learning (ICML), 2025, 2026 (🥈Silver Reviewer)
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2024, 2025, 2026
• AAAI Conference on Artificial Intelligence (AAAI), 2026
• International Conference on Artificial Intelligence and Statistics (AISTATS), 2025, 2026
• ACL Rolling Review (ACL ARR), 2026

Journal Reviewer

• IEEE Transactions on Knowledge and Data Engineering (TKDE)
• IEEE Transactions on Computational Biology and Bioinformatics (TCBB)
• Transactions on Machine Learning Research (TMLR)

Education

Experience