{"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/directed-weight-neural-networks-for-protein","title":"Orientation-Aware Graph Neural Networks for Protein Structure Representation Learning","arxiv_id":"2201.13299","date":"2022-01-28","proceeding":null,"authors":["Jiahan Li","Shitong Luo","Congyue Deng","Chaoran Cheng","Jiaqi Guan","Leonidas Guibas","Jian Peng","Jianzhu Ma"],"abstract":"By folding into particular 3D structures, proteins play a key role in living beings. To learn meaningful representation from a protein structure for downstream tasks, not only the global backbone topology but the local fine-grained orientational relations between amino acids should also be considered. In this work, we propose the Orientation-Aware Graph Neural Networks (OAGNNs) to better sense the geometric characteristics in protein structure (e.g. inner-residue torsion angles, inter-residue orientations). Extending a single weight from a scalar to a 3D vector, we construct a rich set of geometric-meaningful operations to process both the classical and SO(3) representations of a given structure. To plug our designed perceptron unit into existing Graph Neural Networks, we further introduce an equivariant message passing paradigm, showing superior versatility in maintaining SO(3)-equivariance at the global scale. Experiments have shown that our OAGNNs have a remarkable ability to sense geometric orientational features compared to classical networks. OAGNNs have also achieved state-of-the-art performance on various computational biology applications related to protein 3D structures. The code is available at https://github.com/Ced3-han/OAGNN/tree/main.","url_abs":"https://arxiv.org/abs/2201.13299v6","url_pdf":"https://arxiv.org/pdf/2201.13299v6.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":"directed-weight-neural-networks-for-protein","repo_url":"https://github.com/ced3-han/oagnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2201.13299","atlas_url":"https://app.syntology.ai/?focus=2201.13299","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.13299"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ced3-han/oagnn","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":3,"unverified":5},"by_repo_kind":{"official":{"samples":8,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"0e8cf361c5d81874","entry":"construct_3d_basis","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/geometric.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/geometric.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"0e8cf361c5d81874"}},{"code_sha256_prefix":"29d5539ba153502d","entry":"normalize_vector","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/geometric.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/geometric.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"29d5539ba153502d"}},{"code_sha256_prefix":"06175d405523ab18","entry":"project_v2v","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/geometric.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/geometric.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"06175d405523ab18"}},{"code_sha256_prefix":"560b5284810a1afe","entry":"SAttention","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/gconv.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/gconv.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"560b5284810a1afe"}},{"code_sha256_prefix":"3b3af6533cb10a54","entry":"VAttention","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/gconv.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/gconv.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"3b3af6533cb10a54"}},{"code_sha256_prefix":"b0cf6793b32580c7","entry":"rotate_apply","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/linear.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/linear.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b0cf6793b32580c7"}},{"code_sha256_prefix":"e253039723a2f1f8","entry":"safe_norm","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/common.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/common.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e253039723a2f1f8"}},{"code_sha256_prefix":"64ba6b751168bfbe","entry":"vector_input_scalar_linear","repo":"ced3-han/oagnn","repo_kind":"official","path":"modules/linear.py","file_url":"https://github.com/ced3-han/oagnn/blob/HEAD/modules/linear.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"64ba6b751168bfbe"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}