王童 博士
助理教授
2010-2014 山东大学学士
2014-2019 清华大学 博士
2016-2017 哈佛大学 访学博士生
2019-2025 微软研究院 高级研究员
2025-至今 清华大学生命学院 助理教授
2025-至今 清华-北大生命科学联合中心、北京生物结构前沿研究中心 研究员
● 研究兴趣、领域：
本实验室研究围绕“人工智能+生物结构”展开，开发人工智能和深度学习算法和技术对生物和药物分子进行表征学习、性质预测、动态模拟和结构设计，以期揭示生命活动的动态机理和助力药物发现，具体研究方向：
1） 模拟：AI驱动的生物分子动力学模拟，包括分割算法、数据生成、模型训练、系统设计和性质计算等
2） 建模：几何深度学习算法和生物结构基础模型用于大分子的结构表征学习和性质预测
3） 应用：动力学模拟和动态结构模型应用于生物机理发现和药物设计
● 代表性论文：
1. Cui, T.#; Zhou, Y.#; Wang, T.*. Recent advances in artificial intelligence–driven biomolecular dynamics simulations based on machine learning force fields. Curr Opin Struc Biol, 2025, 95, 103191.
2. Zhou, Y.; Wang, T.*. Dynamic insights into the structural evolution of ACE2–RBD interactions through molecular dynamics simulation, Markov state modeling, and large language model mutation prediction. J Chem Phys, 2025, 163,19.
3. Wang, T.#*; He, X.#; Li, M.#; Li, Y.#; Bi, R.; Wang, Y.; Cheng, C.; Shen, X.; Meng, J.; Zhang, H.; Liu, H.; Wang, Z.; Li, S.; Shao, B.*; Liu, T. “Ab initio characterization of protein molecular dynamics with AI2BMD”. Nature, 2024, 635, 1019-1027.
4. Wang, Y.#; Wang, T.#*, Li, S.#; He, X.; Li, M.; Wang, Z.; Zheng, N.; Shao, B.; Liu, T. “Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing”. Nat Commun, 2024, 15 (1), 313. Editors’ Highlights.
5. Li, Y.#; Wang, Y.#; Huang, L.*; Yang, H.; Wei, X.; Zhang, J.*; Wang, T.*; Wang, Z.; Shao, B.; Liu, T. “Long-short-range message-passing: A physics-informed framework to capture non-local interaction for scalable molecular dynamics simulation”. Proc Int Conf Learn Represent (ICLR), 2024.
6. Wang, T.#*; He, X.#; Li, M.#; Shao, B.*; Liu, T. “AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics”. Sci Data, 2023, 10 (1), 549.
7. Wang, Y.#; Li, S.#; Wang, T.*; Shao, B.; Zheng, N.; Liu. T. “Geometric transformer with interatomic positional encoding”. Proc Adv Neural Inf Process Syst (NeurIPS), 2023, 36, 55981-55994.
8. Wang, Z.#; Wu, H.#; Sun, L.; He, X.; Liu, Z.; Shao, B.; Wang, T.*; Liu, T. “Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics”. J Chem Phys, 2023, 159(3). Cover story.
9. Li, Z.#; Zhu, S.#; Shao, B.*; Zeng, X.*; Wang, T.*; Liu, T. “DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning”. Brief Bioinform, 2023, 24 (1), bbac597.
10. Lan, J.#; He, X.#; Ren, Y.#; Wang, Z.#; Zhou, H.; Fan, S.; Zhu, C.; Liu, D.; Shao, B.; Liu, T.; Wang, Q.; Zhang, L.*; Ge, J.*; Wang, T.*; Wang, X.*. “Structural insights into the SARS-CoV-2 Omicron RBD-ACE2 interaction”. Cell Res, 2022, 32, 593-595.
11. Zhang, S.#; Liang, Q.#; He, X.; Zhao, C.; Ren, W.; Yang, Z.; Wang, Z.; Ding, Q.; Deng, H.; Wang, T.*; Zhang, L.*; Wang, X.*. “Loss of Spike N370 glycosylation as an important evolutionary event for the enhanced infectivity of SARS-CoV-2”. Cell Res, 2022, 32, 315-318.
12. Gong, S.#; He, X.#; Meng, Q.; Ma, Z.; Shao, B.*; Wang, T.*; Liu, T. “Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics” J Phys Chem B, 2022, 126 (46), 9465-9475. Cover story.
13. Liu, S.#; Wang, Y.#; Deng, Y.; He, L.; Shao, B.; Yin, J.; Zheng, N.; Liu, T.; Wang, T*. “Improved drug–target interaction prediction with intermolecular graph transformer”. Brief Bioinform, 2022, 23 (5), bbac162.
14. Li, Y.#; Wang, T.#*; Zhang, J.#; Shao, B.; Gong, H.*; Liu, T. “Exploring the Regulatory Function of the N‐terminal Domain of SARS‐CoV‐2 Spike Protein through Molecular Dynamics Simulation”. Adv Theor Simul, 2021, 4 (10), 2100152. Cover story & “Top Downloaded Article” Award.
15. Wang, T.; Qiao, Y.; Ding, W.; Mao, W.; Zhou, Y.*; Gong, H.*. “Improved fragment sampling for ab initio protein structure prediction using deep neural networks”. Nat Mach Intell, 2019, 1 (8), 347-355. Highlighted: Nat Mach Intell, 2019, 1 (8), 336-337.
#: Co-first author; *: Corresponding author
● 荣誉、奖励及参加学术团体的情况：
首都前沿学术成果奖（2025）
2024年度中国生物信息学十大进展（2024）
首届全球AI药物研发大赛冠军（2023）
● 联系方式：
网站：https://www.wanggroup.ai/
电话：+86-10-62794752
电子邮件：tongwang@mail.tsinghua.edu.cn
实验室通讯地址：北京市海淀区清华大学生物医学馆A216-A
