Ke, Guolin

DPA-2: a large atomic model as a multi-task learner

Zhang, D., Liu, X., Zhang, X., Zhang, C., Cai, C., Bi, H., Du, Y., Qin, X., Peng, A., Huang, J., et al. (2023) · arXiv

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 155

Uni-Mol: A Universal 3D Molecular Representation Learning Framework

Zhou, G., Gao, Z., Ding, Q., Zheng, H., Xu, H., Wei, Z., Zhang, L., & Ke, G. (2022) · ChemRxiv

Computational Drug Discovery MethodsAdvanced Chemical Physics Studies

Cited by 81

Less is More: Pretrain a Strong Siamese Encoder for Dense Text Retrieval Using a Weak Decoder

Lu, S., He, D., Xiong, C., Ke, G., Malik, W., Dou, Z., Bennett, P., Liu, T.-Y., & Overwijk, A. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 49

Accurate Conformation Sampling via Protein Structural Diffusion

Fan, J., Li, Z., Alcaide, E., Ke, G., Huang, H., & Weinan, E. (2024) · Journal of Chemical Information and Modeling

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 17

Accurate Conformation Sampling via Protein Structural Diffusion

Fan, J., Li, Z., Alcaide, E., Ke, G., Huang, H., & E, W. (2024) · Journal of Chemical Information and Modeling

Protein Structure and DynamicsComputational Drug Discovery Methods

Cited by 17