ATOM3D: Tasks On Molecules in Three Dimensions

by   Raphael J. L. Townshend, et al.

Computational methods that operate directly on three-dimensional molecular structure hold large potential to solve important questions in biology and chemistry. In particular deep neural networks have recently gained significant attention. In this work we present ATOM3D, a collection of both novel and existing datasets spanning several key classes of biomolecules, to systematically assess such learning methods. We develop three-dimensional molecular learning networks for each of these tasks, finding that they consistently improve performance relative to one- and two-dimensional methods. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, while graph networks perform well on systems requiring detailed positional information. Furthermore, equivariant networks show significant promise. Our results indicate many molecular problems stand to gain from three-dimensional molecular learning. All code and datasets can be accessed via .



There are no comments yet.


page 1

page 2

page 3

page 4


Deeply learning molecular structure-property relationships using graph attention neural network

Molecular structure-property relationships are the key to molecular engi...

Cormorant: Covariant Molecular Neural Networks

We propose Cormorant, a rotationally covariant neural network architectu...

Message-passing neural networks for high-throughput polymer screening

Machine learning methods have shown promise in predicting molecular prop...

Convolutional Networks on Graphs for Learning Molecular Fingerprints

We introduce a convolutional neural network that operates directly on gr...

Are Learned Molecular Representations Ready For Prime Time?

Advancements in neural machinery have led to a wide range of algorithmic...

Generating equilibrium molecules with deep neural networks

Discovery of atomistic systems with desirable properties is a major chal...

Benchmarking Graphormer on Large-Scale Molecular Modeling Datasets

This technical note describes the recent updates of Graphormer, includin...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.