Selected Publications [Full List]
(* indicates equal contribution.)
Exploring Synthesizable Chemical Space with Iterative Pathway Refinements
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Weili Nie, and Arash Vahdat
International Conference on Learning Representations (ICLR), 2026(Oral)
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Xuan Zhang*, Limei Wang*, Jacob Helwig*, Youzhi Luo*, Cong Fu*, Yaochen Xie*, Meng Liu, Yuchao Lin, Zhao Xu, Keqiang Yan, Keir Adams, Maurice Weiler, Xiner Li, Tianfan Fu, Yucheng Wang, Alex Strasser, Haiyang Yu, YuQing Xie, Xiang Fu, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stark, Shurui Gui, Carl Edwards, Nicholas Gao, Adriana Ladera, Tailin Wu, Elyssa F. Hofgard, Aria Mansouri Tehrani, Rui Wang, Ameya Daigavane, Montgomery Bohde, Jerry Kurtin, Qian Huang, Tuong Phung, Minkai Xu, Chaitanya K. Joshi, Simon V. Mathis, Kamyar Azizzadenesheli, Ada Fang, Alan Aspuru-Guzik, Erik Bekkers, Michael Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Lio, Rose Yu, Stephan Gunnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess Smidt, Shuiwang Ji
Foundations and Trends® in Machine Learning, 2025
GenMol: A Drug Discovery Generalist with Discrete Diffusion
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Weili Nie, and Arash Vahdat
International Conference on Machine Learning (ICML), 2025
Molecule Generation with Fragment Retrieval Augmentation
Seul Lee, Karsten Kreis, Srimukh Prasad Veccham, Meng Liu, Danny Reidenbach, Saee Paliwal, Arash Vahdat, and Weili Nie
Advances in Neural Information Processing Systems (NeurIPS), 2024
DiffBP: Generative Diffusion of 3D Molecules for Target Protein Binding
Haitao Lin*, Yufei Huang*, Odin Zhang*, Siqi Ma, Meng Liu, Xuanjing Li, Lirong Wu, Shuiwang Ji, Tingjun Hou, and Stan Z. Li
Chemical Science, 2024
On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods
Montgomery Bohde*, Meng Liu*, Alexandra Saxton, and Shuiwang Ji
International Conference on Learning Representations (ICLR), 2024(Spotlight)
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
Meng Liu, Haiyang Yu, and Shuiwang Ji
Transactions on Machine Learning Research (TMLR), 2024
Video Timeline Modeling for News Story Understanding
Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, and Boqing Gong
Advances in Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2023(Spotlight)
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Haiyang Yu*, Meng Liu*, Youzhi Luo, Alex Strasser, Xiaofeng Qian†, Xiaoning Qian†, and Shuiwang Ji†
Advances in Neural Information Processing Systems (NeurIPS), Track on Datasets and Benchmarks, 2023
Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, and Shuiwang Ji
Advances in Neural Information Processing Systems (NeurIPS), 2023
Graph Mixup with Soft Alignments
Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji†, and Na Zou†
International Conference on Machine Learning (ICML), 2023
Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models
Meng Liu, Haoran Liu, and Shuiwang Ji
International Conference on Learning Representations (ICLR), 2023
Generating 3D Molecules for Target Protein Binding
Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, and Shuiwang Ji
International Conference on Machine Learning (ICML), 2022(Oral, 2.1% acceptance rate)
GraphFM: Improving Large-Scale GNN Training via Feature Momentum
Haiyang Yu*, Limei Wang*, Bokun Wang*, Meng Liu, Tianbao Yang, and Shuiwang Ji
International Conference on Machine Learning (ICML), 2022
Spherical Message Passing for 3D Molecular Graphs
Yi Liu*, Limei Wang*, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, and Shuiwang Ji
International Conference on Learning Representations (ICLR), 2022
Non-Local Graph Neural Networks
Meng Liu*, Zhengyang Wang*, and Shuiwang Ji
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021
DIG: A Turnkey Library for Diving into Graph Deep Learning Research
Meng Liu*, Youzhi Luo*, Limei Wang*, Yaochen Xie*, Hao Yuan*, Shurui Gui*, Haiyang Yu*, Zhao Xu, Jingtun Zhang, Yi Liu, Keqiang Yan, Haoran Liu, Cong Fu, Bora Oztekin, Xuan Zhang, and Shuiwang Ji
Journal of Machine Learning Research (JMLR), 2021
Towards Deeper Graph Neural Networks
Meng Liu, Hongyang Gao, and Shuiwang Ji
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2020
