| Learning to make chemical predictions: the interplay of feature representation, data, and machine learning methods M Haghighatlari, J Li, F Heidar-Zadeh, Y Liu, X Guan, T Head-Gordon Chem 6 (7), 1527-1542, 2020 | 82 | 2020 |
| Newtonnet: A newtonian message passing network for deep learning of interatomic potentials and forces M Haghighatlari, J Li, X Guan, O Zhang, A Das, CJ Stein, F Heidar-Zadeh, ... Digital Discovery 1 (3), 333-343, 2022 | 73 | 2022 |
| Recent advances for improving the accuracy, transferability, and efficiency of reactive force fields I Leven, H Hao, S Tan, X Guan, KA Penrod, D Akbarian, B Evangelisti, ... Journal of chemical theory and computation 17 (6), 3237-3251, 2021 | 53 | 2021 |
| A benchmark dataset for Hydrogen Combustion X Guan, A Das, CJ Stein, F Heidar-Zadeh, L Bertels, M Liu, ... Scientific data 9 (1), 215, 2022 | 10 | 2022 |
| Mechanism of the stereoselective catalysis of Diels–Alderase PyrE3 involved in pyrroindomycin biosynthesis B Li, X Guan, S Yang, Y Zou, W Liu, KN Houk Journal of the American Chemical Society 144 (11), 5099-5107, 2022 | 10 | 2022 |
| Protein C-GeM: A coarse-grained electron model for fast and accurate protein electrostatics prediction X Guan, I Leven, F Heidar-Zadeh, T Head-Gordon Journal of chemical information and modeling 61 (9), 4357-4369, 2021 | 10 | 2021 |
| Leak proof PDBBind: A reorganized dataset of protein-ligand complexes for more generalizable binding affinity prediction J Li, X Guan, O Zhang, K Sun, Y Wang, D Bagni, T Head-Gordon ArXiv, 2023 | 5 | 2023 |
| Learning to make chemical predictions: the interplay of feature representation, data, and machine learning methods. Chem 6: 1527–1542 M Haghighatlari, J Li, F Heidar-Zadeh, Y Liu, X Guan, T Head-Gordon | 5 | 2020 |
| Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity X Guan, JP Heindel, T Ko, C Yang, T Head-Gordon Nature Computational Science 3 (11), 965-974, 2023 | 2 | 2023 |
| Using diffusion maps to analyze reaction dynamics for a hydrogen combustion benchmark dataset T Ko, JP Heindel, X Guan, T Head-Gordon, DB Williams-Young, C Yang Journal of Chemical Theory and Computation 19 (17), 5872-5885, 2023 | 2 | 2023 |
| M-Chem: a modular software package for molecular simulation that spans scientific domains J Witek, JP Heindel, X Guan, I Leven, H Hao, P Naullage, A LaCour, ... Molecular physics 121 (9-10), e2129500, 2023 | 1 | 2023 |
| Beyond potential energy surface benchmarking: a complete application of machine learning to chemical reactivity X Guan, J Heindel, T Ko, C Yang, T Head-Gordon arXiv preprint arXiv:2306.08273, 2023 | | 2023 |
| Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design J Li, O Zhang, Y Wang, K Sun, X Guan, D Bagni, M Haghighatlari, ... arXiv preprint arXiv:2110.01806, 2021 | | 2021 |
| Reinforcement Learning with Real-time Docking of 3D Structures to Cover Chemical Space: Mining for Potent SARS-CoV-2 Main Protease Inhibitors J Li, O Zhang, FL Kearns, M Haghighatlari, C Parks, X Guan, I Leven, ... arXiv preprint arXiv:2110.01806, 2021 | | 2021 |
