Dr. Jian Huang’s research group employs a multidisciplinary approach that incorporates structural biology, medicinal chemistry, and molecular cell biology to investigate the working mechanisms and ligand modulation of disease-related membrane proteins. By integrating structural insights with functional activity analysis, the group aims to design and optimize modulators that precisely target these proteins. Their goal is to achieve precise regulation of target proteins, drive drug innovation, and develop effective therapies for disease prevention and treatment.

Dr. Yang has been deeply involved in the theoretical exploration of computational chemistry, with a particular emphasis on molecular dynamics (MD) simulations. He has published over twenty academic papers in journals such as J. Chem. Theory Comput., J. Physical Chem. Lett. and Phys. Rev. Lett. 

Representative achievements: 

1) Development and Application of Enhanced Sampling: He developed the MetaITS multi-scale enhanced sampling method, which has shown an improvement in sampling efficiency of more than an order of magnitude for specific systems. This method achieved the first reversible ice-water phase transition process in all-atom MD simulations.

2) Deep Reinforcement Learning in Molecular Modelling and Simulation: Dr Yang and collaborators pioneered the introduction of deep reinforcement learning into MD simulations, leading to the development of a series of innovative algorithms.

3) Development of Molecular Dynamics Simulation Software: In collaboration with Huawei Technologies Co., Ltd., Dr Yang’s team developed an AI-based molecular dynamics simulation software, MindSPONGE. He proposed a novel “AI-like” programming architecture for next-generation MD simulation software, and the article (J. Chem. Theory Comput. 2023, 19, 4338-4350) was selected as “Editor’s Choice” and became one of JCTC’s “Most Read Articles” for last 12 months.

Honors

2020 HUAWEI Ascend Expert (HAE) 

2022 MindSpore Senior Technical Evangelist 

2023 MindSpore Technical Committee 

2024 MindSpore Outstanding Mentor Award

1. Zhang, J.; Chen, D.; Xia, Y.; Huang, Y.-P.; Lin, X.; Han, X.; Ni, N.; Wang, Z.; Yu, F.; Yang, L.; Yang, Y. I.; Gao, Y. Q., Artificial Intelligence Enhanced Molecular Simulations. J. Chem. Theory Comput. 2023, 19, 4338-4350. 

2. Li, M.; Zhang, J.; Niu, H.; Lei, Y.-K.; Han, X.; Yang, L.; Ye, Z.; Yang, Y. I.; Gao, Y. Q., Phase Transition between Crystalline Variants of Ordinary Ice. J. Phys. Chem. Lett. 2022, 13, 8601-8606. 

3. Lei, Y.-K.; Zhang, Z.; Han, X.; Yang, Y. I.; Zhang, J.; Gao, Y. Q., Locating Transition Zone in Phase Space. J. Chem. Theory Comput. 2022, 18, 6124-6133. 

4. Zhang, J.; Lei, Y.-K.; Zhang, Z.; Han, X.; Li, M.; Yang, L.; Yang, Y. I.; Gao, Y. Q., Deep Reinforcement Learning of Transition States. Phys. Chem. Chem. Phys. 2021, 23, 6888-6895. 

5. Zhang, J.; Lei, Y.-K.; Yang, Y. I.; Gao, Y. Q., Deep Learning for Variational Multiscale Molecular Modeling. J. Chem. Phys. 2020, 153, 174115. 

6. Zhang, J.; Yang, Y. I.; Noé, F., Targeted Adversarial Learning Optimized Sampling. J. Phys. Chem. Lett. 2019, 10, 5791-5797. 

7. Niu, H.; Yang, Y. I.; Parrinello, M., Temperature Dependence of Homogeneous Nucleation in Ice. Phys. Rev. Lett. 2019, 122, 245501. 

8. Yang, Y. I.; Niu, H.; Parrinello, M., Combining Metadynamics and Integrated Tempering Sampling. J. Phys. Chem. Lett. 2018, 9, 6426-6430.