| Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 920 | 2023 |
| DeepPurpose: a deep learning library for drug–target interaction prediction K Huang, T Fu, LM Glass, M Zitnik, C Xiao, J Sun Bioinformatics 36 (22-23), 5545-5547, 2020 | 426 | 2020 |
| Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development K Huang, T Fu, W Gao, Y Zhao, Y Roohani, J Leskovec, CW Coley, ... arXiv preprint arXiv:2102.09548, 2021 | 317 | 2021 |
| Deep feature for text-dependent speaker verification Y Liu, Y Qian, N Chen, T Fu, Y Zhang, K Yu Speech Communication 73, 1-13, 2015 | 219 | 2015 |
| Machine learning for synthetic data generation: a review Y Lu, M Shen, H Wang, X Wang, C van Rechem, T Fu, W Wei arXiv preprint arXiv:2302.04062, 2024 | 177 | 2024 |
| Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, et al. Scientific discovery in the age of artificial intelligence H Wang, T Fu Nature 620 (7972), 47-60, 2023 | 150 | 2023 |
| Sample efficiency matters: a benchmark for practical molecular optimization W Gao, T Fu, J Sun, C Coley Advances in neural information processing systems 35, 21342-21357, 2022 | 142 | 2022 |
| Artificial intelligence foundation for therapeutic science K Huang, T Fu, W Gao, Y Zhao, Y Roohani, J Leskovec, CW Coley, ... Nature Chemical Biology, 2022 | 135 | 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 | 118 | 2023 |
| MIMOSA: Multi-constraint molecule sampling for molecule optimization T Fu, C Xiao, X Li, LM Glass, J Sun Proceedings of the AAAI Conference on Artificial Intelligence 35 (1), 125-133, 2021 | 95 | 2021 |
| 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 | 90 | 2022 |
| Differentiable scaffolding tree for molecular optimization T Fu, W Gao, C Xiao, J Yasonik, CW Coley, J Sun The International Conference on Learning Representations (ICLR), 2022 | 81 | 2022 |
| CORE: Automatic molecule optimization using copy & refine strategy T Fu, C Xiao, J Sun Proceedings of the AAAI Conference on Artificial Intelligence 34 (01), 638-645, 2020 | 79 | 2020 |
| Reinforced genetic algorithm for structure-based drug design T Fu, W Gao, C Coley, J Sun Advances in Neural Information Processing Systems 35, 12325-12338, 2022 | 67 | 2022 |
| Hint: Hierarchical interaction network for clinical-trial-outcome predictions T Fu, K Huang, C Xiao, LM Glass, J Sun Patterns 3 (4), 2022 | 64 | 2022 |
| Speaker verification with deep features Y Liu, T Fu, Y Fan, Y Qian, K Yu 2014 International joint conference on neural networks (IJCNN), 747-753, 2014 | 54 | 2014 |
| Machine learning-aided generative molecular design Y Du, AR Jamasb, J Guo, T Fu, C Harris, Y Wang, C Duan, P Liò, ... Nature Machine Intelligence, 1-16, 2024 | 44 | 2024 |
| Tandem deep features for text-dependent speaker verification. T Fu, Y Qian, Y Liu, K Yu Interspeech, 1327-1331, 2014 | 40 | 2014 |
| Uncertainty quantification and interpretability for clinical trial approval prediction Y Lu, T Chen, N Hao, C Van Rechem, J Chen, T Fu Health Data Science 4, 0126, 2024 | 37 | 2024 |
| Ddl: Deep dictionary learning for predictive phenotyping T Fu, TN Hoang, C Xiao, J Sun IJCAI: proceedings of the conference 2019, 5857, 2019 | 32 | 2019 |
Jimeng SunProfessor at University of Illinois Urbana-ChampaignE-mail megerősítve itt: illinois.edu
