| Graphdf: A discrete flow model for molecular graph generation Y Luo, K Yan, S Ji International conference on machine learning, 7192-7203, 2021 | 203 | 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 | 146* | 2021 |
| Generating 3d molecules for target protein binding M Liu, Y Luo, K Uchino, K Maruhashi, S Ji arXiv preprint arXiv:2204.09410, 2022 | 134 | 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 | 118 | 2022 |
| 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 | 116 | 2023 |
| An autoregressive flow model for 3d molecular geometry generation from scratch Y Luo, S Ji International conference on learning representations (ICLR), 2022 | 91 | 2022 |
| Stochastic optimization of areas under precision-recall curves with provable convergence Q Qi, Y Luo, Z Xu, S Ji, T Yang Advances in neural information processing systems 34, 1752-1765, 2021 | 86 | 2021 |
| Learning fair graph representations via automated data augmentations H Ling, Z Jiang, Y Luo, S Ji, N Zou International Conference on Learning Representations (ICLR), 2023 | 53 | 2023 |
| 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 | 39 | 2021 |
| 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 | 38 | 2024 |
| Efficient approximations of complete interatomic potentials for crystal property prediction Y Lin, K Yan, Y Luo, Y Liu, X Qian, S Ji International Conference on Machine Learning, 21260-21287, 2023 | 37 | 2023 |
| Automated data augmentations for graph classification International conference on learning representations (ICLR), 2023 | 32* | 2023 |
| Towards symmetry-aware generation of periodic materials Y Luo, C Liu, S Ji Advances in Neural Information Processing Systems 36, 2024 | 19 | 2024 |
| 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 | 16 | 2024 |
| Graph Structure Extrapolation for Out-of-Distribution Generalization X Li, S Gui, Y Luo, S Ji Forty-first International Conference on Machine Learning, 2024 | 16* | 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 | 13 | 2021 |
| MoleculeKit: Machine learning methods for molecular property prediction and drug discovery Z Wang, M Liu, Y Luo, Z Xu, Y Xie, L Wang, L Cai, S Ji arXiv preprint arXiv:2012.01981, 2020 | 6 | 2020 |
| 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, ... Proceedings of the 2024 SIAM International Conference on Data Mining (SDM …, 2024 | 3 | 2024 |
| Geometry Informed Tokenization of Molecules for Language Model Generation X Li, L Wang, Y Luo, C Edwards, S Gui, Y Lin, H Ji, S Ji arXiv preprint arXiv:2408.10120, 2024 | 1 | 2024 |
| Learning Disentangled Equivariant Representation for Explicitly Controllable 3D Molecule Generation H Liu, Y Luo, T Li, J Caverlee, MR Min arXiv preprint arXiv:2412.15086, 2024 | | 2024 |
Shuiwang Ji, Professor and Truchard...Department of Computer Science & Engineering, Texas A&M UniversityVerified email at tamu.edu