{"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/enzynet-enzyme-classification-using-3d","title":"EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation","arxiv_id":"1707.06017","date":"2017-07-19","proceeding":null,"authors":["Afshine Amidi","Shervine Amidi","Dimitrios Vlachakis","Vasileios Megalooikonomou","Nikos Paragios","Evangelia I. Zacharaki"],"abstract":"During the past decade, with the significant progress of computational power\nas well as ever-rising data availability, deep learning techniques became\nincreasingly popular due to their excellent performance on computer vision\nproblems. The size of the Protein Data Bank has increased more than 15 fold\nsince 1999, which enabled the expansion of models that aim at predicting\nenzymatic function via their amino acid composition. Amino acid sequence\nhowever is less conserved in nature than protein structure and therefore\nconsidered a less reliable predictor of protein function. This paper presents\nEnzyNet, a novel 3D-convolutional neural networks classifier that predicts the\nEnzyme Commission number of enzymes based only on their voxel-based spatial\nstructure. The spatial distribution of biochemical properties was also examined\nas complementary information. The 2-layer architecture was investigated on a\nlarge dataset of 63,558 enzymes from the Protein Data Bank and achieved an\naccuracy of 78.4% by exploiting only the binary representation of the protein\nshape. Code and datasets are available at https://github.com/shervinea/enzynet.","url_abs":"http://arxiv.org/abs/1707.06017v1","url_pdf":"http://arxiv.org/pdf/1707.06017v1.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":"enzynet-enzyme-classification-using-3d","repo_url":"https://github.com/shervinea/enzynet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"enzynet-enzyme-classification-using-3d","repo_url":"https://github.com/edraizen/molmimic","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1707.06017","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}