Publications
# Co-first authors, * Corresponding authors
Cui, T.#, Zhou, Y.#, Wang, T.* (2025). Recent advances in artificial intelligence–driven biomolecular dynamics simulations based on machine learning force fields. Curr. Opin. Struct. Biol., 95, 103191. (Invited Review)
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. Y. (2024). Ab initio characterization of protein molecular dynamics with AI2BMD. Nature, 635: 1019–1027. (Top 10 Advances in Bioinformatics)
Zhang, H.#, Liu, S.#, You, J., Liu, C.*, Zheng, S.*, Lu, Z., Wang, T., Zheng, N., Shao, B.* (2024). Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning. Nat. Comput. Sci., 4(3): 210–223.
Wang, Y.#, Wang, T.#*, Li, S.#, He, X., Li, M., Wang, Z., Zheng, N., Shao, B.*, Liu, T. Y. (2024). Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing. Nat. Commun., 15(1): 313. (Editors' Highlights: AI and machine learning, Biotechnology and methods)
Li, Y.#, Wang, Y.#, Huang, L.*, Yang, H., Wei, X., Zhang, J.*, Wang, T.*, Wang, Z., Shao, B., Liu, T. Y. (2024). Long-short-range message-passing: A physics-informed framework to capture non-local interaction for scalable molecular dynamics simulation. Proceedings of the 12th International Conference on Learning Representations (ICLR).
Yu, S.*, Wang, Z., Li, Q., Wang, T., Zhao, W. (2024). Innovative application of a novel di-d-fructofuranose 1,2':2,3'-dianhydride hydrolase (DFA-IIIase) from Duffyella gerundensis A4 to burdock root to improve nutrition. Food Funct., 15(2): 1021–1030.
Wang, Z., Liu, G., Zhou, Y., Wang, T.*, Shao, B.* (2023). Efficiently incorporating quintuple interactions into geometric deep learning force fields. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), 36: 77043–77055.
Wang, Y.#, Li, S.#, Wang, T.*, Shao, B., Zheng, N., Liu, T. Y. (2023). Geometric transformer with interatomic positional encoding. Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), 36: 55981–55994.
Wang, T.#*, He, X.#, Li, M.#, Shao, B.*, Liu, T. Y. (2023). AIMD-Chig: Exploring the conformational space of a 166-atom protein Chignolin with ab initio molecular dynamics. Sci. Data, 10(1): 549.
Wang, Z., Wu, H., Sun, L., He, X., Liu, Z., Shao, B., Wang, T.*, Liu, T. Y. (2023). Improving machine learning force fields for molecular dynamics simulations with fine-grained force metrics. J. Chem. Phys., 159(3): 034102. (2023 JCP Emerging Investigators Special Collection, Cover)
Li, Z.#, Zhu, S.#, Shao, B.*, Zeng, X.*, Wang, T.*, Liu, T. Y. (2023). DSN-DDI: an accurate and generalized framework for drug–drug interaction prediction by dual-view representation learning. Brief. Bioinform., 24(1): bbac597. (ESI highly cited paper)
Gong, S.#, He, X.#, Meng, Q., Ma, Z., Shao, B.*, Wang, T.*, Liu, T. Y. (2022). Stochastic lag time parameterization for Markov state models of protein dynamics. J. Phys. Chem. B, 126(46): 9465–9475.
Wang, Y., Li, S., Wang, Z., He, X., Shao, B., Liu, T. Y., Wang, T.* (2022). An ensemble of VisNet, Transformer-M, and pretraining models for molecular property prediction in OGB Large-Scale Challenge@ NeurIPS 2022. arXiv preprint arXiv:2211.12791. (2nd place in OGB-LSC@NeurIPS 2022)
Liu, S.#, Wang, Y.#, Deng, Y., He, L., Shao, B., Yin, J., Zheng, N., Liu, T. Y., Wang, T.* (2022). Improved drug–target interaction prediction with intermolecular graph transformer. Brief. Bioinform., 23(5): bbac162.
Zhang, S.#, Liang, Q.#, He, X., Zhao, C., Ren, W., Yang, Z., Wang, Z., Ding, Q., Deng, H., Wang, T.*, Zhang, L.*, Wang, X.* (2022). Loss of Spike N370 glycosylation as an important evolutionary event for the enhanced infectivity of SARS-CoV-2. Cell Res., 32(3): 315–318.
Lan, J.#, He, X.#, Ren, Y.#, Wang, Z.#, Zhou, H., Fan, S., Zhu, C., Liu, D., Shao, B., Liu, T. Y., Wang, Q., Zhang, L.*, Ge, J.*, Wang, T.*, Wang, X.* (2022). Structural insights into the SARS-CoV-2 Omicron RBD–ACE2 interaction. Cell Res., 32(6): 593–595.
Ding, W.#, Xu, Q.#, Liu, S., Wang, T.*, Shao, B., Gong, H., Liu, T. Y. (2021). SAMF: a self-adaptive protein modeling framework. Bioinformatics, 37(22): 4075–4082.
Li, Y., Wang, T.*, Zhang, J., Shao, B., Gong, H.*, Wang, Y., He, X., Liu, S., Liu, T. Y. (2021). Exploring the regulatory function of the N-terminal domain of SARS-CoV-2 Spike protein through molecular dynamics simulation. Adv. Theory Simul., 4(10): 2100152. (Top Downloaded Article Award, Cover)
Liu, S., Wang, T.*, Xu, Q., Shao, B., Yin, J., Liu, T. Y. (2021). Complementing sequence-derived features with structural information extracted from fragment libraries for protein structure prediction. BMC Bioinformatics, 22(1): 351.
Wang, T., Qiao, Y., Ding, W., Mao, W., Zhou, Y.*, Gong, H.* (2019). Improved fragment sampling for ab initio protein structure prediction using deep neural networks. Nat. Mach. Intell., 1(8): 347–355. (Editors' Highlights: Protein structure prediction beyond AlphaFold)
Wang, T., Gong, H.*, Shakhnovich, E. I.* (2019). Improved fragment-based movement with LRFragLib for all-atom Ab initio protein folding. arXiv preprint arXiv:1906.05785.
Mao, W., Wang, T., Zhang, W., Gong, H.* (2018). Identification of residue pairing in interacting β-strands from a predicted residue contact map. BMC Bioinformatics, 19(1): 146.
Wang, T., Yang, Y., Zhou, Y., Gong, H.* (2017). LRFragLib: an effective algorithm to identify fragments for de novo protein structure prediction. Bioinformatics, 33(5): 677–684.
Wang, T., Sui, L., Kang, C.* (2013). Syntenin as a multifunctional cellular adaptor protein. Chin. J. Biochem. Mol. Biol., 12: 6–14.
Jiang, H. S., Sun, C., Wang, T., Zhao, X. F., Wang, J. X.* (2013). A single whey acidic protein domain containing protein (SWD) inhibits bacteria invasion and dissemination in shrimp Marsupenaeus japonicus. Fish Shellfish Immunol., 35(2): 310–318.