王童 博士

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）

主要科研领域与方向

本实验室研究围绕“人工智能+生物分子结构”展开，利用深度学习对生物大分子和药物分子进行结构表征学习、性质和互作预测、动力学模拟和序列设计，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） AI驱动的生物分子动力学模拟算法设计和应用

2） 图神经网络、几何深度学习和机器学习力场的算法开发

3） AI辅助药物发现和分子性质预测

代表性论文

1.     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.

2.     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.

3.     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.

4.     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.

5.     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.

6.     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.

7.     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. 

8.     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.

9.     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.

10.   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.

11.   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.

12.   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.

13.   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.