Meng Liu

According to our database, Meng Liu authored at least 27 papers between 2020 and 2024.

Collaborative distances:

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

2024

Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm.

Meng Liu, Haiyang Yu, Shuiwang Ji

Trans. Mach. Learn. Res., 2024

BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery.

Peter St. John, Dejun Lin, Polina Binder, Malcolm Greaves, Vega Shah, John St. John, Adrian Lange, Patrick Hsu, Rajesh Illango, Arvind Ramanathan, Anima Anandkumar, David H. Brookes, Akosua Busia, Abhishaike Mahajan, Stephen Malina, Neha Prasad, Sam Sinai, Lindsay Edwards, Thomas Gaudelet, Cristian Regep, Martin Steinegger, Burkhard Rost, Alexander Brace, Kyle Hippe, Luca Naef, Keisuke Kamata, George Armstrong, Kevin Boyd, Zhonglin Cao, Han-Yi Chou, Simon Chu, Allan dos Santos Costa, Sajad Darabi, Eric Dawson, Kieran Didi, Cong Fu, Mario Geiger, Michelle Gill, Darren Hsu, Gagan Kaushik, Maria Korshunova, Steven Kothen-Hill, Youhan Lee, Meng Liu, Micha Livne, Zachary McClure, Jonathan Mitchell, Alireza Moradzadeh, Ohad Mosafi, Youssef Nashed, Saee Paliwal, Yuxing Peng, Sara Rabhi, Farhad Ramezanghorbani, Danny Reidenbach, Camir Ricketts, Brian Roland, Kushal Shah, Tyler Shimko, Hassan Sirelkhatim, Savitha Srinivasan, Abraham C. Stern, Dorota Toczydlowska, Srimukh Prasad Veccham, Niccolò Alberto Elia Venanzi, Anton Vorontsov, Jared Wilber, Isabel Wilkinson, Wei Jing Wong, Eva Xue, Cory Ye, Xin Yu, Yang Zhang, Guoqing Zhou, Becca Zandstein, Christian Dallago, Bruno Trentini, Emine Küçükbenli, Timur Rvachov, Eddie Calleja, Johnny Israeli, Harry Clifford, Risto Haukioja, Nicholas Haemel, Kyle Tretina, Neha Tadimeti, Anthony B. Costa

CoRR, 2024

3D Molecular Geometry Analysis with 2D Graphs.

Zhao Xu, Yaochen Xie, Youzhi Luo, Xuan Zhang, Xinyi Xu, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, Shuiwang Ji

Proceedings of the 2024 SIAM International Conference on Data Mining, 2024

On the Markov Property of Neural Algorithmic Reasoning: Analyses and Methods.

Montgomery Bohde, Meng Liu, Alexandra Saxton, Shuiwang Ji

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

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, Haiyang Yu, Yuqing Xie, Xiang Fu, Alex Strasser, Shenglong Xu, Yi Liu, Yuanqi Du, Alexandra Saxton, Hongyi Ling, Hannah Lawrence, Hannes Stärk, 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, Alán Aspuru-Guzik, Erik J. Bekkers, Michael M. Bronstein, Marinka Zitnik, Anima Anandkumar, Stefano Ermon, Pietro Liò, Rose Yu, Stephan Günnemann, Jure Leskovec, Heng Ji, Jimeng Sun, Regina Barzilay, Tommi S. Jaakkola, Connor W. Coley, Xiaoning Qian, Xiaofeng Qian, Tess E. Smidt, Shuiwang Ji

CoRR, 2023

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules.

Haiyang Yu, Meng Liu, Youzhi Luo, Alex Strasser, Xiaofeng Qian, Xiaoning Qian, Shuiwang Ji

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

Video Timeline Modeling For News Story Understanding.

Meng Liu, Mingda Zhang, Jialu Liu, Hanjun Dai, Ming-Hsuan Yang, Shuiwang Ji, Zheyun Feng, Boqing Gong

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

Joint Learning of Label and Environment Causal Independence for Graph Out-of-Distribution Generalization.

Shurui Gui, Meng Liu, Xiner Li, Youzhi Luo, Shuiwang Ji

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

Graph and Geometry Generative Modeling for Drug Discovery.

Minkai Xu, Meng Liu, Wengong Jin, Shuiwang Ji, Jure Leskovec, Stefano Ermon

Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Graph Mixup with Soft Alignments.

Hongyi Ling, Zhimeng Jiang, Meng Liu, Shuiwang Ji, Na Zou

Proceedings of the International Conference on Machine Learning, 2023

Gradient-Guided Importance Sampling for Learning Binary Energy-Based Models.

Meng Liu, Haoran Liu, Shuiwang Ji

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Non-Local Graph Neural Networks.

Meng Liu, Zhengyang Wang, Shuiwang Ji

IEEE Trans. Pattern Anal. Mach. Intell., 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

Your Neighbors Are Communicating: Towards Powerful and Scalable Graph Neural Networks.

Meng Liu, Haiyang Yu, Shuiwang Ji

CoRR, 2022

Advanced graph and sequence neural networks for molecular property prediction and drug discovery.

Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Qi Qi, Zhuoning Yuan, Tianbao Yang, Shuiwang Ji

Bioinform., 2022

Neighbor2Seq: Deep Learning on Massive Graphs by Transforming Neighbors to Sequences.

Meng Liu, Shuiwang Ji

Proceedings of the 2022 SIAM International Conference on Data Mining, 2022

Frontiers of Graph Neural Networks with DIG.

Shuiwang Ji, Meng Liu, Yi Liu, Youzhi Luo, Limei Wang, Yaochen Xie, Zhao Xu, Haiyang Yu

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

GraphFM: Improving Large-Scale GNN Training via Feature Momentum.

Haiyang Yu, Limei Wang, Bokun Wang, Meng Liu, Tianbao Yang, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2022

Generating 3D Molecules for Target Protein Binding.

Meng Liu, Youzhi Luo, Kanji Uchino, Koji Maruhashi, Shuiwang Ji

Proceedings of the International Conference on Machine Learning, 2022

Spherical Message Passing for 3D Molecular Graphs.

Yi Liu, Limei Wang, Meng Liu, Yuchao Lin, Xuan Zhang, Bora Oztekin, Shuiwang Ji

Proceedings of the Tenth International Conference on Learning Representations, 2022

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, Shuiwang Ji

J. Mach. Learn. Res., 2021

Molecule3D: A Benchmark for Predicting 3D Geometries from Molecular Graphs.

Zhao Xu, Youzhi Luo, Xuan Zhang, Xinyi Xu, Yaochen Xie, Meng Liu, Kaleb Dickerson, Cheng Deng, Maho Nakata, Shuiwang Ji

CoRR, 2021

Fast Quantum Property Prediction via Deeper 2D and 3D Graph Networks.

Meng Liu, Cong Fu, Xuan Zhang, Limei Wang, Yaochen Xie, Hao Yuan, Youzhi Luo, Zhao Xu, Shenglong Xu, Shuiwang Ji

CoRR, 2021

Spherical Message Passing for 3D Graph Networks.

Yi Liu, Limei Wang, Meng Liu, Xuan Zhang, Bora Oztekin, Shuiwang Ji

CoRR, 2021

GraphEBM: Molecular Graph Generation with Energy-Based Models.

Meng Liu, Keqiang Yan, Bora Oztekin, Shuiwang Ji

CoRR, 2021

2020

MoleculeKit: Machine Learning Methods for Molecular Property Prediction and Drug Discovery.

Zhengyang Wang, Meng Liu, Youzhi Luo, Zhao Xu, Yaochen Xie, Limei Wang, Lei Cai, Shuiwang Ji

CoRR, 2020

Towards Deeper Graph Neural Networks.

Meng Liu, Hongyang Gao, Shuiwang Ji

Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020