| Incorporating explicit water molecules and ligand conformation stability in machine-learning scoring functions J Lu, X Hou, C Wang, Y Zhang Journal of chemical information and modeling 59 (11), 4540-4549, 2019 | 78 | 2019 |
| Predicting molecular energy using force-field optimized geometries and atomic vector representations learned from an improved deep tensor neural network J Lu, C Wang, Y Zhang Journal of chemical theory and computation 15 (7), 4113-4121, 2019 | 29 | 2019 |
| Dataset construction to explore chemical space with 3D geometry and deep learning J Lu, S Xia, J Lu, Y Zhang Journal of chemical information and modeling 61 (3), 1095-1104, 2021 | 18 | 2021 |
| Exploring fragment-based target-specific ranking protocol with machine learning on cathepsin S Y Yang, J Lu, C Yang, Y Zhang Journal of computer-aided molecular design 33, 1095-1105, 2019 | 15 | 2019 |
| Computational strategy for bound state structure prediction in structure-based virtual screening: a case study of protein tyrosine phosphatase receptor type O inhibitors X Hou, D Rooklin, D Yang, X Liang, K Li, J Lu, C Wang, P Xiao, Y Zhang, ... Journal of chemical information and modeling 58 (11), 2331-2342, 2018 | 14 | 2018 |
| Mechanistic insights into the rate-limiting step in purine-specific nucleoside hydrolase N Chen, Y Zhao, J Lu, R Wu, Z Cao Journal of Chemical Theory and Computation 11 (7), 3180-3188, 2015 | 9 | 2015 |
| Integrating Machine Learning into Protein-Ligand Scoring Function Development J Lu New York University, 2020 | | 2020 |
| Incorporating ligand conformation stability and explicit water molecules in machine-learning scoring functions J Lu, X Hou, C Wang, Y Zhang ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 257, 2019 | | 2019 |
Yingkai ZhangProfessor of Chemistry, New York UniversityVerified email at nyu.edu
