Towards deeper graph neural networks M Liu, H Gao, S Ji Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 570 | 2020 |
Spherical message passing for 3d molecular graphs Y Liu, L Wang, M Liu, Y Lin, X Zhang, B Oztekin, S Ji International Conference on Learning Representations (ICLR), 2022 | 266* | 2022 |
Non-local graph neural networks M Liu, Z Wang, S Ji IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 | 171 | 2021 |
DIG: A turnkey library for diving into graph deep learning research M Liu, Y Luo, L Wang, Y Xie, H Yuan, S Gui, H Yu, Z Xu, J Zhang, Y Liu, ... Journal of Machine Learning Research 22 (240), 1-9, 2021 | 123* | 2021 |
Generating 3d molecules for target protein binding M Liu, Y Luo, K Uchino, K Maruhashi, S Ji International Conference on Machine Learning, 13912-13924, 2022 | 101 | 2022 |
Advanced graph and sequence neural networks for molecular property prediction and drug discovery Z Wang, M Liu, Y Luo, Z Xu, Y Xie, L Wang, L Cai, Q Qi, Z Yuan, T Yang, ... Bioinformatics 38 (9), 2579-2586, 2022 | 98 | 2022 |
Graphebm: Molecular graph generation with energy-based models M Liu, K Yan, B Oztekin, S Ji arXiv preprint arXiv:2102.00546, 2021 | 87 | 2021 |
Artificial intelligence for science in quantum, atomistic, and continuum systems X Zhang, L Wang, J Helwig, Y Luo, C Fu, Y Xie, M Liu, Y Lin, Z Xu, K Yan, ... arXiv preprint arXiv:2307.08423, 2023 | 71 | 2023 |
Diffbp: Generative diffusion of 3d molecules for target protein binding H Lin, Y Huang, M Liu, X Li, S Ji, SZ Li arXiv preprint arXiv:2211.11214, 2022 | 56 | 2022 |
GraphFM: Improving large-scale GNN training via feature momentum H Yu, L Wang, B Wang, M Liu, T Yang, S Ji International Conference on Machine Learning, 25684-25701, 2022 | 25 | 2022 |
Molecule3d: A benchmark for predicting 3d geometries from molecular graphs Z Xu, Y Luo, X Zhang, X Xu, Y Xie, M Liu, K Dickerson, C Deng, M Nakata, ... arXiv preprint arXiv:2110.01717, 2021 | 25 | 2021 |
Graph mixup with soft alignments H Ling, Z Jiang, M Liu, S Ji, N Zou International Conference on Machine Learning, 21335-21349, 2023 | 21 | 2023 |
Joint learning of label and environment causal independence for graph out-of-distribution generalization S Gui, M Liu, X Li, Y Luo, S Ji Advances in Neural Information Processing Systems 36, 2024 | 17 | 2024 |
Fast quantum property prediction via deeper 2d and 3d graph networks M Liu, C Fu, X Zhang, L Wang, Y Xie, H Yuan, Y Luo, Z Xu, S Xu, S Ji arXiv preprint arXiv:2106.08551, 2021 | 12 | 2021 |
Qh9: A quantum hamiltonian prediction benchmark for qm9 molecules H Yu, M Liu, Y Luo, A Strasser, X Qian, X Qian, S Ji Advances in Neural Information Processing Systems 36, 2024 | 9 | 2024 |
Graph and geometry generative modeling for drug discovery M Xu, M Liu, W Jin, S Ji, J Leskovec, S Ermon Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023 | 4 | 2023 |
Neighbor2seq: Deep learning on massive graphs by transforming neighbors to sequences M Liu, S Ji Proceedings of the 2022 SIAM International Conference on Data Mining (SDM …, 2022 | 4 | 2022 |
On the markov property of neural algorithmic reasoning: Analyses and methods M Bohde, M Liu, A Saxton, S Ji arXiv preprint arXiv:2403.04929, 2024 | 3 | 2024 |
Empowering GNNs via Edge-Aware Weisfeiler-Lehman Algorithm M Liu, H Yu, S Ji | 3* | 2022 |
3D Molecular Geometry Analysis with 2D Graphs Z Xu, Y Xie, Y Luo, X Zhang, X Xu, M Liu, K Dickerson, C Deng, M Nakata, ... arXiv preprint arXiv:2305.13315, 2023 | 2 | 2023 |