| Multi-constraint molecular generation based on conditional transformer, knowledge distillation and reinforcement learning J Wang, CY Hsieh, M Wang, X Wang, Z Wu, D Jiang, B Liao, X Zhang, ... Nature Machine Intelligence 3 (10), 914-922, 2021 | 99 | 2021 |
| Boosting protein–ligand binding pose prediction and virtual screening based on residue–atom distance likelihood potential and graph transformer C Shen, X Zhang, Y Deng, J Gao, D Wang, L Xu, P Pan, T Hou, Y Kang Journal of Medicinal Chemistry 65 (15), 10691-10706, 2022 | 62 | 2022 |
| Can machine learning consistently improve the scoring power of classical scoring functions? Insights into the role of machine learning in scoring functions C Shen, Y Hu, Z Wang, X Zhang, H Zhong, G Wang, X Yao, L Xu, D Cao, ... Briefings in Bioinformatics 22 (1), 497-514, 2021 | 60 | 2021 |
| Beware of the generic machine learning-based scoring functions in structure-based virtual screening C Shen, Y Hu, Z Wang, X Zhang, J Pang, G Wang, H Zhong, L Xu, D Cao, ... Briefings in Bioinformatics 22 (3), bbaa070, 2021 | 49 | 2021 |
| Knowledge-based BERT: a method to extract molecular features like computational chemists Z Wu, D Jiang, J Wang, X Zhang, H Du, L Pan, CY Hsieh, D Cao, T Hou Briefings in Bioinformatics 23 (3), bbac131, 2022 | 35 | 2022 |
| VAD-MM/GBSA: a variable atomic dielectric MM/GBSA model for improved accuracy in protein–ligand binding free energy calculations E Wang, W Fu, D Jiang, H Sun, J Wang, X Zhang, G Weng, H Liu, P Tao, ... Journal of Chemical Information and Modeling 61 (6), 2844-2856, 2021 | 31 | 2021 |
| Accuracy or novelty: what can we gain from target-specific machine-learning-based scoring functions in virtual screening? C Shen, G Weng, X Zhang, ELH Leung, X Yao, J Pang, X Chai, D Li, ... Briefings in Bioinformatics 22 (5), bbaa410, 2021 | 30 | 2021 |
| Identification of active molecules against Mycobacterium tuberculosis through machine learning Q Ye, X Chai, D Jiang, L Yang, C Shen, X Zhang, D Li, D Cao, T Hou Briefings in Bioinformatics 22 (5), bbab068, 2021 | 29 | 2021 |
| The impact of cross-docked poses on performance of machine learning classifier for protein–ligand binding pose prediction C Shen, X Hu, J Gao, X Zhang, H Zhong, Z Wang, L Xu, Y Kang, D Cao, ... Journal of cheminformatics 13, 1-18, 2021 | 20 | 2021 |
| ResGen is a pocket-aware 3D molecular generation model based on parallel multiscale modelling O Zhang, J Zhang, J Jin, X Zhang, RL Hu, C Shen, H Cao, H Du, Y Kang, ... Nature Machine Intelligence 5 (9), 1020-1030, 2023 | 16 | 2023 |
| Efficient and accurate large library ligand docking with KarmaDock X Zhang, O Zhang, C Shen, W Qu, S Chen, H Cao, Y Kang, Z Wang, ... Nature Computational Science 3 (9), 789-804, 2023 | 14 | 2023 |
| TocoDecoy: a new approach to design unbiased datasets for training and benchmarking machine-learning scoring functions X Zhang, C Shen, B Liao, D Jiang, J Wang, Z Wu, H Du, T Wang, W Huo, ... Journal of Medicinal Chemistry 65 (11), 7918-7932, 2022 | 14 | 2022 |
| A generalized protein–ligand scoring framework with balanced scoring, docking, ranking and screening powers C Shen, X Zhang, CY Hsieh, Y Deng, D Wang, L Xu, J Wu, D Li, Y Kang, ... Chemical Science 14 (30), 8129-8146, 2023 | 12 | 2023 |
| Proteome-wide profiling of the covalent-Druggable cysteines with a structure-based deep graph learning network H Du, D Jiang, J Gao, X Zhang, L Jiang, Y Zeng, Z Wu, C Shen, L Xu, ... Research, 2022 | 11 | 2022 |
| ASFP (Artificial Intelligence based Scoring Function Platform): a web server for the development of customized scoring functions X Zhang, C Shen, X Guo, Z Wang, G Weng, Q Ye, G Wang, Q He, B Yang, ... Journal of Cheminformatics 13, 1-9, 2021 | 10 | 2021 |
| Discovery of novel non-steroidal selective glucocorticoid receptor modulators by structure-and IGN-based virtual screening, structural optimization, and biological evaluation X Hu, J Pang, C Chen, D Jiang, C Shen, X Chai, L Yang, X Zhang, L Xu, ... European Journal of Medicinal Chemistry 237, 114382, 2022 | 8 | 2022 |
| MetalProGNet: a structure-based deep graph model for metalloprotein–ligand interaction predictions D Jiang, Z Ye, CY Hsieh, Z Yang, X Zhang, Y Kang, H Du, Z Wu, J Wang, ... Chemical Science 14 (8), 2054-2069, 2023 | 6 | 2023 |
| CarsiDock: a deep learning paradigm for accurate protein–ligand docking and screening based on large-scale pre-training H Cai, C Shen, T Jian, X Zhang, T Chen, X Han, Z Yang, W Dang, ... Chemical Science 15 (4), 1449-1471, 2024 | 3 | 2024 |
| TB-IECS: an accurate machine learning-based scoring function for virtual screening X Zhang, C Shen, D Jiang, J Zhang, Q Ye, L Xu, T Hou, P Pan, Y Kang Journal of Cheminformatics 15 (1), 63, 2023 | 3 | 2023 |
| Topology-based and conformation-based decoys database: an unbiased online database for training and benchmarking machine-learning scoring functions X Zhang, C Shen, T Wang, Y Kang, D Li, P Pan, J Wang, G Wang, Y Deng, ... Journal of Medicinal Chemistry 66 (13), 9174-9183, 2023 | 2 | 2023 |
