| Could graph neural networks learn better molecular representation for drug discovery? A comparison study of descriptor-based and graph-based models D Jiang, Z Wu, CY Hsieh, G Chen, B Liao, Z Wang, C Shen, D Cao, J Wu, ... Journal of cheminformatics 13, 1-23, 2021 | 400 | 2021 |
| From machine learning to deep learning: Advances in scoring functions for protein–ligand docking C Shen, J Ding, Z Wang, D Cao, X Ding, T Hou Wiley Interdisciplinary Reviews: Computational Molecular Science 10 (1), e1429, 2020 | 214 | 2020 |
| PROTAC-DB: an online database of PROTACs G Weng, C Shen, D Cao, J Gao, X Dong, Q He, B Yang, D Li, J Wu, T Hou Nucleic acids research 49 (D1), D1381-D1387, 2021 | 121 | 2021 |
| Interactiongraphnet: A novel and efficient deep graph representation learning framework for accurate protein–ligand interaction predictions D Jiang, CY Hsieh, Z Wu, Y Kang, J Wang, E Wang, B Liao, C Shen, L Xu, ... Journal of medicinal chemistry 64 (24), 18209-18232, 2021 | 119 | 2021 |
| ADMET evaluation in drug discovery. 19. Reliable prediction of human cytochrome P450 inhibition using artificial intelligence approaches Z Wu, T Lei, C Shen, Z Wang, D Cao, T Hou Journal of chemical information and modeling 59 (11), 4587-4601, 2019 | 102 | 2019 |
| Do we need different machine learning algorithms for QSAR modeling? A comprehensive assessment of 16 machine learning algorithms on 14 QSAR data sets Z Wu, M Zhu, Y Kang, ELH Leung, T Lei, C Shen, D Jiang, Z Wang, D Cao, ... Briefings in bioinformatics 22 (4), bbaa321, 2021 | 99 | 2021 |
| Combined strategies in structure-based virtual screening Z Wang, H Sun, C Shen, X Hu, J Gao, D Li, D Cao, T Hou Physical Chemistry Chemical Physics 22 (6), 3149-3159, 2020 | 99 | 2020 |
| Deep learning approaches for de novo drug design: An overview M Wang, Z Wang, H Sun, J Wang, C Shen, G Weng, X Chai, H Li, D Cao, ... Current opinion in structural biology 72, 135-144, 2022 | 85 | 2022 |
| A magic drug target: Androgen receptor D Li, W Zhou, J Pang, Q Tang, B Zhong, C Shen, L Xiao, T Hou Medicinal research reviews 39 (5), 1485-1514, 2019 | 66 | 2019 |
| 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 | 65 | 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 |
| ADMET evaluation in drug discovery. 20. Prediction of breast cancer resistance protein inhibition through machine learning D Jiang, T Lei, Z Wang, C Shen, D Cao, T Hou Journal of Cheminformatics 12, 1-26, 2020 | 58 | 2020 |
| PROTAC-DB 2.0: an updated database of PROTACs G Weng, X Cai, D Cao, H Du, C Shen, Y Deng, Q He, B Yang, D Li, T Hou Nucleic acids research 51 (D1), D1367-D1372, 2023 | 56 | 2023 |
| 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 | 50 | 2021 |
| Relation: A deep generative model for structure-based de novo drug design M Wang, CY Hsieh, J Wang, D Wang, G Weng, C Shen, X Yao, Z Bing, ... Journal of Medicinal Chemistry 65 (13), 9478-9492, 2022 | 47 | 2022 |
| Improving docking-based virtual screening ability by integrating multiple energy auxiliary terms from molecular docking scoring WL Ye, C Shen, GL Xiong, JJ Ding, AP Lu, TJ Hou, DS Cao Journal of Chemical Information and Modeling 60 (9), 4216-4230, 2020 | 44 | 2020 |
| Improving structure-based virtual screening performance via learning from scoring function components GL Xiong, WL Ye, C Shen, AP Lu, TJ Hou, DS Cao Briefings in bioinformatics 22 (3), bbaa094, 2021 | 39 | 2021 |
| Comprehensive assessment of nine docking programs on type II kinase inhibitors: prediction accuracy of sampling power, scoring power and screening power C Shen, Z Wang, X Yao, Y Li, T Lei, E Wang, L Xu, F Zhu, D Li, T Hou Briefings in bioinformatics 21 (1), 282-297, 2020 | 33 | 2020 |
| Featurization strategies for protein–ligand interactions and their applications in scoring function development G Xiong, C Shen, Z Yang, D Jiang, S Liu, A Lu, X Chen, T Hou, D Cao Wiley Interdisciplinary Reviews: Computational Molecular Science 12 (2), e1567, 2022 | 32 | 2022 |
| 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 | 31 | 2021 |
