| Atomo: Communication-efficient learning via atomic sparsification H Wang, S Sievert, S Liu, Z Charles, D Papailiopoulos, S Wright Advances in Neural Information Processing Systems (NeurIPS) 2018 31, 2018 | 400 | 2018 |
| Pre-training Molecular Graph Representation with 3D Geometry S Liu, H Wang, W Liu, J Lasenby, H Guo, J Tang International Conference on Learning Representations (ICLR) 2022, 2021 | 313 | 2021 |
| N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules S Liu, MF Demirel, Y Liang Advances in Neural Information Processing Systems (NeurIPS) 2019 32, 2019 | 201 | 2019 |
| Learning to navigate the synthetically accessible chemical space using reinforcement learning SK Gottipati, B Sattarov, S Niu, Y Pathak, H Wei, S Liu, S Blackburn, ... International Conference on Machine Learning (ICML) 2021, 3668-3679, 2020 | 152 | 2020 |
| Loss-Balanced Task Weighting to Reduce Negative Transfer in Multi-Task Learning S Liu, Y Liang, A Gitter AAAI-SA 2019, 2019 | 151 | 2019 |
| Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing S Liu, W Nie, C Wang, J Lu, Z Qiao, L Liu, J Tang, C Xiao, A Anandkumar Nature Machine Intelligence 2023 5 (12), 1447-1457, 2023 | 115 | 2023 |
| Learning drug functions from chemical structures with convolutional neural networks and random forests JG Meyer, S Liu, IJ Miller, JJ Coon, A Gitter Journal of Chemical Information and Modeling (JCIM) 2019 59 (10), 4438-4449, 2019 | 100 | 2019 |
| Bad Global Minima Exist and SGD Can Reach Them S Liu, D Papailiopoulos, D Achlioptas Advances in Neural Information Processing Systems (NeurIPS) 2020 33, 8543-8552, 2020 | 89 | 2020 |
| Molgensurvey: A systematic survey in machine learning models for molecule design Y Du, T Fu, J Sun, S Liu arXiv preprint arXiv:2203.14500, 2022 | 77 | 2022 |
| 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, ... arXiv preprint arXiv:2202.08320, 2022 | 77 | 2022 |
| Practical Model Selection for Prospective Virtual Screening S Liu, M Alnammi, SS Ericksen, AF Voter, GE Ananiev, JL Keck, ... Journal of Chemical Information and Modeling (JCIM) 2019 59 (1), 282-293, 2018 | 72 | 2018 |
| Molecular Geometry Pretraining with SE(3)-invariant Denoising Distance Matching S Liu, H Guo, J Tang International Conference on Learning Representations (ICLR) 2023, 2022 | 68 | 2022 |
| Gimlet: A unified graph-text model for instruction-based molecule zero-shot learning H Zhao, S Liu, M Chang, H Xu, J Fu, Z Deng, L Kong, Q Liu Advances in Neural Information Processing Systems 36, 5850-5887, 2023 | 50 | 2023 |
| A Text-guided Protein Design Framework S Liu, Y Li, Z Li, A Gitter, Y Zhu, J Lu, Z Xu, W Nie, A Ramanathan, C Xiao, ... arXiv preprint arXiv:2302.04611, 2023 | 44 | 2023 |
| Conversational Drug Editing Using Retrieval and Domain Feedback S Liu, J Wang, Y Yang, C Wang, L Liu, H Guo, C Xiao International Conference on Learning Representations (ICLR) 2024, 2023 | 36* | 2023 |
| Structured Multi-task Learning for Molecular Property Prediction S Liu, M Qu, Z Zhang, H Cai, J Tang International Conference on Artificial Intelligence and Statistics (AISTATS …, 2022 | 31* | 2022 |
| Shaping the water-harvesting behavior of metal–organic frameworks aided by fine-tuned GPT models Z Zheng, AH Alawadhi, S Chheda, SE Neumann, N Rampal, S Liu, ... Journal of the American Chemical Society 145 (51), 28284-28295, 2023 | 30 | 2023 |
| Symmetry-Informed Geometric Representation for Molecules, Proteins, and Crystalline Materials S Liu, W Du, Y Li, Z Li, Z Zheng, C Duan, Z Ma, O Yaghi, A Anandkumar, ... Advances in Neural Information Processing Systems (NeurIPS) 2023, 2023 | 25 | 2023 |
| A Group Symmetric Stochastic Differential Equation Model for Molecule Multi-modal Pretraining S Liu, W Du, Z Ma, H Guo, J Tang International Conference on Machine Learning (ICML) 2023, 2023 | 23 | 2023 |
| Neural sentence ordering based on constraint graphs Y Zhu, K Zhou, JY Nie, S Liu, Z Dou AAAI 2021 35 (16), 14656-14664, 2021 | 21 | 2021 |
Jian Tang (唐建）Associate Professor, Mila-Quebec AI Institute, HEC Montréal, Canada CIFAR AI ChairVerified email at hec.ca
