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ATOM3D: Tasks on Molecules in Three Dimensions
Raphael Townshend · Martin Vögele · Patricia Suriana · Alex Derry · Alexander Powers · Yianni Laloudakis · Sidhika Balachandar · Bowen Jing · Brandon Anderson · Stephan Eismann · Risi Kondor · Russ Altman · Ron Dror

Wed Dec 08 08:20 AM -- 08:30 AM (PST) @
in Dataset and Benchmark Track 2 »

Computational methods that operate on three-dimensional molecular structure have the potential to solve important questions in biology and chemistry. In particular, deep neural networks have gained significant attention, but their widespread adoption in the biomolecular domain has been limited by a lack of either systematic performance benchmarks or a unified toolkit for interacting with molecular data. To address this, we present ATOM3D, a collection of both novel and existing benchmark datasets spanning several key classes of biomolecules. We implement several classes of three-dimensional molecular learning methods for each of these tasks and show that they consistently improve performance relative to methods based on one- and two-dimensional representations. The specific choice of architecture proves to be critical for performance, with three-dimensional convolutional networks excelling at tasks involving complex geometries, graph networks performing well on systems requiring detailed positional information, and the more recently developed equivariant networks showing significant promise. Our results indicate that many molecular problems stand to gain from three-dimensional molecular learning, and that there is potential for improvement on many tasks which remain underexplored. To lower the barrier to entry and facilitate further developments in the field, we also provide a comprehensive suite of tools for dataset processing, model training, and evaluation in our open-source atom3d Python package. All datasets are available for download from www.atom3d.ai.

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Author Information

Raphael Townshend (Stanford University)
Martin Vögele (Stanford University)
Patricia Suriana (Stanford University)
Alex Derry (Stanford University)
Alexander Powers
Yianni Laloudakis
Sidhika Balachandar (Stanford University)
Bowen Jing (Stanford University)
Brandon Anderson
Stephan Eismann (Stanford University)
Risi Kondor (Flatiron Institute)

Risi Kondor joined the Flatiron Institute in 2019 as a Senior Research Scientist with the Center for Computational Mathematics. Previously, Kondor was an Associate Professor in the Department of Computer Science, Statistics, and the Computational and Applied Mathematics Initiative at the University of Chicago. His research interests include computational harmonic analysis and machine learning. Kondor holds a Ph.D. in Computer Science from Columbia University, an MS in Knowledge Discovery and Data Mining from Carnegie Mellon University, and a BA in Mathematics from the University of Cambridge. He also holds a diploma in Computational Fluid Dynamics from the Von Karman Institute for Fluid Dynamics and a diploma in Physics from Eötvös Loránd University in Budapest.

Russ Altman
Ron Dror (Stanford University)

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