no code implementations • 31 Dec 2023 • Sihao Yuan, Xu Han, Jun Zhang, Zhaoxin Xie, Cheng Fan, Yunlong Xiao, Yi Qin Gao, Yi Isaac Yang
We applied this approach to study a Claisen rearrangement reaction and a Carbonyl insertion reaction catalyzed by Manganese.
no code implementations • 2 May 2023 • Jun Zhang, Xiaohan Lin, Weinan E, Yi Qin Gao
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales.
no code implementations • 16 Mar 2023 • Yupeng Huang, Hong Zhang, Siyuan Jiang, Dajiong Yue, Xiaohan Lin, Jun Zhang, Yi Qin Gao
In this study, we take the advantage of both traditional and machine-learning based methods, and present a method Deep Site and Docking Pose (DSDP) to improve the performance of blind docking.
2 code implementations • 20 Aug 2022 • Jun Zhang, Sirui Liu, Mengyun Chen, Haotian Chu, Min Wang, Zidong Wang, Jialiang Yu, Ningxi Ni, Fan Yu, Diqing Chen, Yi Isaac Yang, Boxin Xue, Lijiang Yang, YuAn Liu, Yi Qin Gao
Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development.
2 code implementations • 24 Jun 2022 • Sirui Liu, Jun Zhang, Haotian Chu, Min Wang, Boxin Xue, Ningxi Ni, Jialiang Yu, Yuhao Xie, Zhenyu Chen, Mengyun Chen, YuAn Liu, Piya Patra, Fan Xu, Jie Chen, Zidong Wang, Lijiang Yang, Fan Yu, Lei Chen, Yi Qin Gao
We provide in addition the benchmark training procedure for SOTA protein structure prediction model on this dataset.
no code implementations • 22 Dec 2020 • Jun Zhang, Yao-Kun Lei, Yaqiang Zhou, Yi Isaac Yang, Yi Qin Gao
Deep learning is changing many areas in molecular physics, and it has shown great potential to deliver new solutions to challenging molecular modeling problems.
no code implementations • 13 Nov 2020 • Jun Zhang, Yao-Kun Lei, Zhen Zhang, Xu Han, Maodong Li, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao
Combining reinforcement learning (RL) and molecular dynamics (MD) simulations, we propose a machine-learning approach (RL$^\ddag$) to automatically unravel chemical reaction mechanisms.
no code implementations • 25 Apr 2020 • Jun Zhang, Yao-Kun Lei, Zhen Zhang, Junhan Chang, Maodong Li, Xu Han, Lijiang Yang, Yi Isaac Yang, Yi Qin Gao
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems.
no code implementations • 7 Jun 2019 • Jun Zhang, Yao-Kun Lei, Xing Che, Zhen Zhang, Yi Isaac Yang, Yi Qin Gao
In this paper we first analyzed the inductive bias underlying the data scattered across complex free energy landscapes (FEL), and exploited it to train deep neural networks which yield reduced and clustered representation for the FEL.