{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/atom3d-tasks-on-molecules-in-three-dimensions-1","title":"ATOM3D: Tasks On Molecules in Three Dimensions","arxiv_id":"2012.04035","date":"2020-12-07","proceeding":null,"authors":["Raphael J. L. Townshend","Martin Vögele","Patricia Suriana","Alexander Derry","Alexander Powers","Yianni Laloudakis","Sidhika Balachandar","Bowen Jing","Brandon Anderson","Stephan Eismann","Risi Kondor","Russ B. Altman","Ron O. Dror"],"abstract":"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 https://www.atom3d.ai .","url_abs":"https://arxiv.org/abs/2012.04035v4","url_pdf":"https://arxiv.org/pdf/2012.04035v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"atom3d-tasks-on-molecules-in-three-dimensions-1","repo_url":"https://github.com/drorlab/atom3d","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"atom3d-tasks-on-molecules-in-three-dimensions-1","repo_url":"https://github.com/drorlab/gvp-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"atom3d-tasks-on-molecules-in-three-dimensions-1","repo_url":"https://github.com/smiles724/protmd","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"atom3d-benchmark","task_name":"Atom3D benchmark"}],"methods":[],"datasets_introduced":[{"slug":"atom3d","name":"ATOM3D","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2012.04035","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2012.04035"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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