Search Results for author:
Found 13 papers, 9 papers with code
Pocket2Mol: Efficient Molecular Sampling Based on 3D Protein Pockets
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
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
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
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
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.
Score-Based Point Cloud Denoising
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.
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
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.
EBM-Fold: Fully-Differentiable Protein Folding Powered by Energy-based Models
In this paper, we propose a fully-differentiable approach for protein structure optimization, guided by a data-driven generative network.
Learning Gradient Fields for Molecular Conformation Generation
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
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.
Learning Neural Generative Dynamics for Molecular Conformation Generation
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
Point cloud generation thus amounts to learning the reverse diffusion process that transforms the noise distribution to the distribution of a desired shape.
Differentiable Manifold Reconstruction for Point Cloud Denoising
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.
