Search Results for author: Shitong Luo

Found 13 papers, 9 papers with code

Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets

3 code implementations15 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.


Equivariant Point Cloud Analysis via Learning Orientations for Message Passing

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.

A 3D Generative Model for Structure-Based Drug Design

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.


Predicting Molecular Conformation via Dynamic Graph Score Matching

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.

Deep Point Set Resampling via Gradient Fields

no code implementations3 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.

Autonomous Driving Denoising +1

Score-Based Point Cloud Denoising (Learning Gradient Fields for Point Cloud Denoising)

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.

Denoising Surface Reconstruction

An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming

1 code implementation15 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.

Bilevel Optimization

EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models

no code implementations11 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.

Denoising Protein Folding +1

Learning Gradient Fields for Molecular Conformation Generation

6 code implementations9 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.


Diffusion Probabilistic Models for 3D Point Cloud Generation

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.

Data Augmentation Point Cloud Generation

Learning Neural Generative Dynamics for Molecular Conformation Generation

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.


A Point Cloud Generative Model Based on Nonequilibrium Thermodynamics

no code implementations1 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.

Point Cloud Generation

Differentiable Manifold Reconstruction for Point Cloud Denoising

1 code implementation27 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.

Denoising Surface Reconstruction

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