| Encoding the atomic structure for machine learning in materials science S Li, Y Liu, D Chen, Y Jiang, Z Nie, F Pan Wiley Interdisciplinary Reviews: Computational Molecular Science 12 (1), e1558, 2022 | 71 | 2022 |
| Generative models for inverse design of inorganic solid materials L Chen, W Zhang, Z Nie, S Li, F Pan Journal of Materials Informatics 1 (1), N/A-N/A, 2021 | 39 | 2021 |
| Construction and Application of Materials Knowledge Graph Based on Author Disambiguation: Revisiting the Evolution of LiFePO4 Z Nie, Y Liu, L Yang, S Li, F Pan Advanced Energy Materials 11 (16), 2003580, 2021 | 29 | 2021 |
| Automating materials exploration with a semantic knowledge graph for Li‐ion battery cathodes Z Nie, S Zheng, Y Liu, Z Chen, S Li, K Lei, F Pan Advanced Functional Materials 32 (26), 2201437, 2022 | 26 | 2022 |
| Running ahead of evolution—AI-based simulation for predicting future high-risk SARS-CoV-2 variants J Chen, Z Nie, Y Wang, K Wang, F Xu, Z Hu, B Zheng, Z Wang, G Song, ... IJHPCA, ACM Gordon Bell COVID 2022 Finalist 37 (6), 650-665, 2023 | 7 | 2023 |
| Interpretable antibody-antigen interaction prediction by introducing route and priors guidance Y Liu bioRxiv, 2024 | 2 | 2024 |
| ProtFAD: Introducing function-aware domains as implicit modality towards protein function perception M Wang, Z Nie, Y He, Z Ren arXiv preprint arXiv:2405.15158, 2024 | 1 | 2024 |
| Semantic knowledge graph as a companion for catalyst recommendation Z Zhang, S Ma, S Zheng, Z Nie, B Wang, K Lei, S Li, F Pan National Science Open 3 (2), 20230040, 2024 | 1 | 2024 |
| Hunting for peptide binders of specific targets with data-centric generative language models Z Nie, D Li, J Chen, F Xu, Y Liu, J Fu, X Liu, Z Wang, Y Ma, K Wang, ... bioRxiv, 2023.12. 31.573750, 2024 | 1 | 2024 |
| Evolution-guided large language model is a predictor of virus mutation trends Z Nie, X Liu, J Chen, Z Wang, Y Liu, H Si, T Dong, F Xu, G Song, Y Wang, ... bioRxiv, 2023 | 1 | 2023 |
| A unified evolution-driven deep learning framework for virus variation driver prediction Z Nie, X Liu, J Chen, Z Wang, Y Liu, H Si, T Dong, F Xu, G Song, Y Wang, ... Nature Machine Intelligence, 1-14, 2025 | | 2025 |
| Multi-purpose enzyme-substrate interaction prediction with progressive conditional deep learning Z Nie, H Zhang, H Jiang, Y Liu, X Huang, F Xu, Y Tian, J Chen, WB Zhang | | 2024 |
| Towards anchoring evolutionary fitness for protein stability with virtual chemical environment recovery Z Nie, Y Ma, J Chen, Y Liu, Z Liu, P Yang, F Xu, X Huang, F Yin, Z Li, J Fu, ... bioRxiv, 2024.04. 22.590665, 2024 | | 2024 |
| Deep-learning-enabled generative prevalent mutation prediction through host-to-herd in silico virus evolution X Liu, Z Nie, H Si, X Shen, Y Liu, X Huang, T Dong, F Xu, J Chen, P Zhou, ... bioRxiv, 2024.11. 28.625962, 2024 | | 2024 |
| E2VD: a unified evolution-driven framework for virus variation drivers prediction Z Nie, X Liu, J Chen, Z Wang, Y Liu, H Si, T Dong, F Xu, G Song, Y Wang, ... bioRxiv, 2023.11. 27.568815, 2023 | | 2023 |
| Deep Learning Methods for Protein Representation and Function Prediction: A Comprehensive Overview Z Ren | | |
Yonghong TianProfessor, School of CS and School of ECE at Peking University, Pengcheng Laboratory, IEEE Fellowpku.edu.cn의 이메일 확인됨