| Comparative assessment of scoring functions: the CASF-2016 update M Su, Q Yang, Y Du, G Feng, Z Liu, Y Li, R Wang Journal of chemical information and modeling 59 (2), 895-913, 2018 | 638 | 2018 |
| Comparative assessment of scoring functions on a diverse test set T Cheng, X Li, Y Li, Z Liu, R Wang Journal of chemical information and modeling 49 (4), 1079-1093, 2009 | 603 | 2009 |
| PDB-wide collection of binding data: current status of the PDBbind database Z Liu, Y Li, L Han, J Li, J Liu, Z Zhao, W Nie, Y Liu, R Wang Bioinformatics 31 (3), 405-412, 2015 | 587 | 2015 |
| Forging the basis for developing protein–ligand interaction scoring functions Z Liu, M Su, L Han, J Liu, Q Yang, Y Li, R Wang Accounts of chemical research 50 (2), 302-309, 2017 | 449 | 2017 |
| Comparative assessment of scoring functions on an updated benchmark: 2. Evaluation methods and general results Y Li, L Han, Z Liu, R Wang Journal of chemical information and modeling 54 (6), 1717-1736, 2014 | 403 | 2014 |
| Comparative assessment of scoring functions on an updated benchmark: 1. Compilation of the test set Y Li, Z Liu, J Li, L Han, J Liu, Z Zhao, R Wang Journal of chemical information and modeling 54 (6), 1700-1716, 2014 | 245 | 2014 |
| Evaluation of the performance of four molecular docking programs on a diverse set of protein‐ligand complexes X Li, Y Li, T Cheng, Z Liu, R Wang Journal of computational chemistry 31 (11), 2109-2125, 2010 | 165 | 2010 |
| Assessing protein–ligand interaction scoring functions with the CASF-2013 benchmark Y Li, M Su, Z Liu, J Li, J Liu, L Han, R Wang Nature protocols 13 (4), 666-680, 2018 | 115 | 2018 |
| Tapping on the black box: how is the scoring power of a machine-learning scoring function dependent on the training set? M Su, G Feng, Z Liu, Y Li, R Wang Journal of chemical information and modeling 60 (3), 1122-1136, 2020 | 68 | 2020 |
| Test MM-PB/SA on true conformational ensembles of protein− ligand complexes Y Li, Z Liu, R Wang Journal of chemical information and modeling 50 (9), 1682-1692, 2010 | 43 | 2010 |
| AutoT&T v. 2: an efficient and versatile tool for lead structure generation and optimization Y Li, Z Zhao, Z Liu, M Su, R Wang Journal of chemical information and modeling 56 (2), 435-453, 2016 | 35 | 2016 |
| A knowledge-guided strategy for improving the accuracy of scoring functions in binding affinity prediction T Cheng, Z Liu, R Wang BMC bioinformatics 11, 1-16, 2010 | 28 | 2010 |
| Automatic tailoring and transplanting: a practical method that makes virtual screening more useful Y Li, Y Zhao, Z Liu, R Wang Journal of chemical information and modeling 51 (6), 1474-1491, 2011 | 24 | 2011 |
| Enhance the performance of current scoring functions with the aid of 3D protein-ligand interaction fingerprints J Liu, M Su, Z Liu, J Li, Y Li, R Wang BMC bioinformatics 18, 1-22, 2017 | 21 | 2017 |
| Mining the characteristic interaction patterns on protein–protein binding interfaces Y Li, Z Liu, L Han, C Li, R Wang Journal of chemical information and modeling 53 (9), 2437-2447, 2013 | 17 | 2013 |
| Cross‐Mapping of Protein–Ligand Binding Data Between ChEMBL and PDBbind Z Liu, J Li, J Liu, Y Liu, W Nie, L Han, Y Li, R Wang Molecular Informatics 34 (8), 568-576, 2015 | 12 | 2015 |
| Development of a new benchmark for assessing the scoring functions applicable to protein–protein interactions L Han, Q Yang, Z Liu, Y Li, R Wang Future medicinal chemistry 10 (13), 1555-1574, 2018 | 10 | 2018 |
| Theoretical Analysis of Fas Ligand‐Induced Apoptosis with an Ordinary Differential Equation Model Z Shi, Y Li, Z Liu, J Mi, R Wang Molecular informatics 31 (11‐12), 793-807, 2012 | 4 | 2012 |
| A Statistical Survey on the Binding Constants of Covalently Bound Protein–Ligand Complexes X Li, Z Liu, Y Li, J Li, J Li, R Wang Molecular Informatics 29 (1‐2), 87-96, 2010 | 3 | 2010 |
| An Efficient and Versatile Tool for Automatic Fragment-Based Design Y Li, Z Zhao, Z Liu, M Su, R Wang 第十届全国化学生物学学术会议报告摘要集, 2017 | | 2017 |
