no code implementations • 3 Mar 2024 • Zihan Zhou, Ruiying Liu, Jiachen Zheng, Xiaoxue Wang, Tianshu Yu
Sampling viable 3D structures (e. g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherein SE(3)-invariant properties can be naturally characterized by the inter-point distance manifold.
no code implementations • 7 Oct 2023 • Zihan Zhou, Ruiying Liu, Tianshu Yu
Diffusion-based generative models in SE(3)-invariant space have demonstrated promising performance in molecular conformation generation, but typically require solving stochastic differential equations (SDEs) with thousands of update steps.
no code implementations • 12 Sep 2023 • Zihan Zhou, Ruiying Liu, Chaolong Ying, Ruimao Zhang, Tianshu Yu
Molecular conformation generation, a critical aspect of computational chemistry, involves producing the three-dimensional conformer geometry for a given molecule.
no code implementations • 3 Feb 2020 • Hongyu Li, Zifei Liang, Chaoyi Zhang, Ruiying Liu, Jing Li, Weihong Zhang, Dong Liang, Bowen Shen, Xiaoliang Zhang, Yulin Ge, Jiangyang Zhang, Leslie Ying
We also demonstrate that the trained neural network is robust to noise and motion in the testing data, and the network trained using healthy volunteer data can be directly applied to stroke patient data without compromising the lesion detectability.
