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.
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.
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.
no code implementations • NeurIPS 2021 • Shitong Luo, Chence Shi, Minkai Xu, Jian Tang
However, these non-bonded atoms may be proximal to each other in 3D space, and modeling their interactions is of crucial importance to accurately determine molecular conformations, especially for large molecules and multi-molecular complexes.
no code implementations • 3 Nov 2021 • Haolan Chen, Bi'an Du, Shitong Luo, Wei Hu
3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc.
2 code implementations • ICCV 2021 • Shitong Luo, Wei Hu
Since $p * n$ is unknown at test-time, and we only need the score (i. e., the gradient of the log-probability function) to perform gradient ascent, we propose a neural network architecture to estimate the score of $p * n$ given only noisy point clouds as input.
1 code implementation • 15 May 2021 • Minkai Xu, Wujie Wang, Shitong Luo, Chence Shi, Yoshua Bengio, Rafael Gomez-Bombarelli, Jian Tang
Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program.
no code implementations • 11 May 2021 • Jiaxiang Wu, Shitong Luo, Tao Shen, Haidong Lan, Sheng Wang, Junzhou Huang
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
6 code implementations • 9 May 2021 • Chence Shi, Shitong Luo, Minkai Xu, Jian Tang
We study a fundamental problem in computational chemistry known as molecular conformation generation, trying to predict stable 3D structures from 2D molecular graphs.
3 code implementations • CVPR 2021 • Shitong Luo, Wei Hu
We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.
3 code implementations • ICLR 2021 • Minkai Xu, Shitong Luo, Yoshua Bengio, Jian Peng, Jian Tang
Inspired by the recent progress in deep generative models, in this paper, we propose a novel probabilistic framework to generate valid and diverse conformations given a molecular graph.
no code implementations • 1 Jan 2021 • Shitong Luo, Wei Hu
Point cloud generation thus amounts to learning the reverse diffusion process that transforms the noise distribution to the distribution of a desired shape.
1 code implementation • 27 Jul 2020 • Shitong Luo, Wei Hu
Afterwards, the decoder infers the underlying manifold by transforming each sampled point along with the embedded feature of its neighborhood to a local surface centered around the point.