| Autoint: Automatic feature interaction learning via self-attentive neural networks W Song, C Shi, Z Xiao, Z Duan, Y Xu, M Zhang, J Tang International Conference on Information and Knowledge Management (CIKM 2019 …, 2019 | 854 | 2019 |
| GeoDiff: A Geometric Diffusion Model for Molecular Conformation Generation M Xu, L Yu, Y Song, C Shi, S Ermon, J Tang International Conference on Learning Representations (ICLR 2022), 2022 | 512 | 2022 |
| GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation C Shi*, M Xu*, Z Zhu, W Zhang, M Zhang, J Tang International Conference on Learning Representations (ICLR 2020), 2020 | 480 | 2020 |
| Learning gradient fields for molecular conformation generation C Shi*, S Luo*, M Xu, J Tang International Conference on Machine Learning (ICML 2021), 2021 | 225 | 2021 |
| A Graph to Graphs Framework for Retrosynthesis Prediction C Shi, M Xu, H Guo, M Zhang, J Tang International Conference on Machine Learning (ICML 2020), 2020 | 172 | 2020 |
| MARS: Markov Molecular Sampling for Multi-objective Drug Discovery Y Xie, C Shi, H Zhou, Y Yang, W Zhang, Y Yu, L Li International Conference on Learning Representations (ICLR 2021), 2021 | 157 | 2021 |
| Predicting Molecular Conformation via Dynamic Graph Score Matching S Luo*, C Shi*, M Xu, J Tang Advances in Neural Information Processing Systems (NeurIPS 2021), 2021 | 106 | 2021 |
| An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming M Xu, W Wang, S Luo, C Shi, Y Bengio, R Gomez-Bombarelli, J Tang International Conference on Machine Learning (ICML 2021), 2021 | 94 | 2021 |
| TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery Z Zhu, C Shi, Z Zhang, S Liu, M Xu, X Yuan, Y Zhang, J Chen, H Cai, J Lu, ... preprint, 2022 | 80 | 2022 |
| Protein Sequence and Structure Co-Design with Equivariant Translation C Shi, C Wang, J Lu, B Zhong, J Tang International Conference on Learning Representations (ICLR 2023), 2023 | 42 | 2023 |
| E3Bind: An End-to-End Equivariant Network for Protein-Ligand Docking Y Zhang*, H Cai*, C Shi, B Zhong, J Tang International Conference on Learning Representations (ICLR 2023), 2023 | 33 | 2023 |
| Non-Autoregressive Electron Redistribution Modeling for Reaction Prediction H Bi*, H Wang*, C Shi, C Coley, J Tang, H Guo International Conference on Machine Learning (ICML 2021), 2021 | 25 | 2021 |
| Structure Language Models for Protein Conformation Generation J Lu, X Chen, SZ Lu, C Shi, H Guo, Y Bengio, J Tang arXiv preprint arXiv:2410.18403, 2024 | 1 | 2024 |
| Design of Ligand-Binding Proteins with Atomic Flow Matching J Liu, S Li, C Shi, Z Yang, J Tang arXiv preprint arXiv:2409.12080, 2024 | | 2024 |
| Fusing Neural and Physical: Augment Protein Conformation Sampling with Tractable Simulations J Lu, Z Zhang, B Zhong, C Shi, J Tang arXiv preprint arXiv:2402.10433, 2024 | | 2024 |
Jian Tang (唐建）Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI ChairVerified email at hec.ca
