Equivariant Graph Neural Networks for 3D Macromolecular Structure

06/07/2021
by   Bowen Jing, et al.
0

Representing and reasoning about 3D structures of macromolecules is emerging as a distinct challenge in machine learning. Here, we extend recent work on geometric vector perceptrons and apply equivariant graph neural networks to a wide range of tasks from structural biology. Our method outperforms all reference architectures on 4 out of 8 tasks in the ATOM3D benchmark and broadly improves over rotation-invariant graph neural networks. We also demonstrate that transfer learning can improve performance in learning from macromolecular structure.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
04/17/2020

Bridging the Gap Between Spectral and Spatial Domainsin Graph Neural Networks

Bridging the Gap Between Spectral and Spatial Domainsin Graph Neural Net...
research
09/03/2020

Learning from Protein Structure with Geometric Vector Perceptrons

Learning on 3D structures of large biomolecules is emerging as a distinc...
research
10/23/2019

Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules

Predicting the relationship between a molecule's structure and its odor ...
research
12/21/2022

Forecasting West Nile Virus with Graph Neural Networks: Harnessing Spatial Dependence in Irregularly Sampled Geospatial Data

Machine learning methods have seen increased application to geospatial e...
research
06/22/2020

Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments

Most reinforcement learning approaches used in behavior generation utili...
research
05/24/2023

Multi-State RNA Design with Geometric Multi-Graph Neural Networks

Computational RNA design has broad applications across synthetic biology...
research
08/12/2021

Room Classification on Floor Plan Graphs using Graph Neural Networks

We present our approach to improve room classification task on floor pla...

Please sign up or login with your details

Forgot password? Click here to reset