{"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/three-dimensionally-embedded-graph","title":"Three-Dimensionally Embedded Graph Convolutional Network (3DGCN) for Molecule Interpretation","arxiv_id":"1811.09794","date":"2018-11-24","proceeding":null,"authors":["Hyeoncheol Cho","Insung S. Choi"],"abstract":"We present a three-dimensional graph convolutional network (3DGCN), which\npredicts molecular properties and biochemical activities, based on 3D molecular\ngraph. In the 3DGCN, graph convolution is unified with learning operations on\nthe vector to handle the spatial information from molecular topology. The 3DGCN\nmodel exhibits significantly higher performance on various tasks compared with\nother deep-learning models, and has the ability of generalizing a given\nconformer to targeted features regardless of its rotations in the 3D space.\nMore significantly, our model also can distinguish the 3D rotations of a\nmolecule and predict the target value, depending upon the rotation degree, in\nthe protein-ligand docking problem, when trained with orientation-dependent\ndatasets. The rotation distinguishability of 3DGCN, along with rotation\nequivariance, provides a key milestone in the implementation of\nthree-dimensionality to the field of deep-learning chemistry that solves\nchallenging biochemical problems.","url_abs":"http://arxiv.org/abs/1811.09794v4","url_pdf":"http://arxiv.org/pdf/1811.09794v4.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":"three-dimensionally-embedded-graph","repo_url":"https://github.com/blackmints/3DGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"molecule-interpretation","task_name":"Molecule Interpretation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.09794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.09794"}},"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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