3 code implementations • NeurIPS 2021 • Shitong Luo, Jiaqi Guan, Jianzhu Ma, Jian Peng
In this paper, we propose a 3D generative model that generates molecules given a designated 3D protein binding site.
3 code implementations • 15 May 2022 • Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, Jianzhu Ma
Deep generative models have achieved tremendous success in designing novel drug molecules in recent years.
2 code implementations • 6 Mar 2023 • Jiaqi Guan, Wesley Wei Qian, Xingang Peng, Yufeng Su, Jian Peng, Jianzhu Ma
Rich data and powerful machine learning models allow us to design drugs for a specific protein target \textit{in silico}.
1 code implementation • 26 Feb 2024 • Jiaqi Guan, Xiangxin Zhou, Yuwei Yang, Yu Bao, Jian Peng, Jianzhu Ma, Qiang Liu, Liang Wang, Quanquan Gu
Designing 3D ligands within a target binding site is a fundamental task in drug discovery.
1 code implementation • CVPR 2022 • Shitong Luo, Jiahan Li, Jiaqi Guan, Yufeng Su, Chaoran Cheng, Jian Peng, Jianzhu Ma
In this work, we propose a novel and simple framework to achieve equivariance for point cloud analysis based on the message passing (graph neural network) scheme.
1 code implementation • 11 May 2023 • Xingang Peng, Jiaqi Guan, Qiang Liu, Jianzhu Ma
Deep generative models have recently achieved superior performance in 3D molecule generation.
no code implementations • 10 Oct 2017 • Jiaqi Guan, Yang Liu, Qiang Liu, Jian Peng
Deep neural networks have been remarkable successful in various AI tasks but often cast high computation and energy cost for energy-constrained applications such as mobile sensing.
no code implementations • 6 Dec 2018 • Jiaqi Guan, Runzhe Li, Sheng Yu, Xuegong Zhang
It can also be used as a data augmentation method to assist studies based on real EMR data.
no code implementations • CVPR 2020 • Jiaqi Guan, Ye Yuan, Kris M. Kitani, Nicholas Rhinehart
Automatically reasoning about future human behaviors is a difficult problem but has significant practical applications to assistive systems.
no code implementations • ICLR 2022 • Jiaqi Guan, Wesley Wei Qian, Qiang Liu, Wei-Ying Ma, Jianzhu Ma, Jian Peng
Assuming different forms of the underlying potential energy function, we can not only reinterpret and unify many of the existing models but also derive new variants of SE(3)-equivariant neural networks in a principled manner.