57 citations · 182 across the 11 of their papers we have counts for
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physics.chem-ph2025
TRIDS: AI-native molecular docking framework for accelerating high-throughput virtual screening with physically valid binding poses
Xuhan Liu, Baohua Zhang, Yue Xue +7
Molecular docking is a cornerstone of drug discovery for unveiling the mechanism of ligand-receptor interactions. With the recent advances of deep learning (DL), AI-powered molecul…
physics.chem-ph2022
Enhanced sampling reveals the main pathway of an organic cascade reaction
Yi Sun, Xu Han, Lijiang Yang
Normal molecular dynamics simulations are usually unable to simulate chemical reactions due to the low probability of forming the transition state. Therefore, enhanced sampling met…
physics.chem-ph2020★ 42 cited
Deep Reinforcement Learning of Transition States
Jun Zhang, Yao-Kun Lei, Zhen Zhang +5
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL) to automatically unravel chemical reaction mechanisms…