{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:FGRSS5ZQQHQLC7KKRSZC5NVAZI","short_pith_number":"pith:FGRSS5ZQ","schema_version":"1.0","canonical_sha256":"29a329773081e0b17d4a8cb22eb6a0ca0480b188bdcbc444cb3fdc58de53830f","source":{"kind":"arxiv","id":"2202.09891","version":2},"attestation_state":"computed","paper":{"title":"Equivariant Graph Attention Networks for Molecular Property Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Djork-Arn\\'e Clevert, Frank No\\'e, Tuan Le","submitted_at":"2022-02-20T19:07:29Z","abstract_excerpt":"Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through the propagation of information between nodes leveraging higher-order representations to faithfully express the geometry of the data, such as directionality in their intermediate layers. In this work, we propose an equivariant GNN that operates with Ca"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2202.09891","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-20T19:07:29Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"d558e150ebecdc7cd6177a1f5506173912cc9a09baa2fb3888397bf9e9fb5baf","abstract_canon_sha256":"84212af49b6fd680d12cb8afc4cc6a186f6ccf7b64d666b648f167400d9c0864"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:01:34.701049Z","signature_b64":"GLtNOxBJcO3kQr8i0f12xPMwfSfrHx88XoMiHwYFlBYbzn3nqx35nKrkIIcY6LC6syzoERqOrgBpQzPi4I4zDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"29a329773081e0b17d4a8cb22eb6a0ca0480b188bdcbc444cb3fdc58de53830f","last_reissued_at":"2026-07-05T04:01:34.700691Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:01:34.700691Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Equivariant Graph Attention Networks for Molecular Property Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Djork-Arn\\'e Clevert, Frank No\\'e, Tuan Le","submitted_at":"2022-02-20T19:07:29Z","abstract_excerpt":"Learning and reasoning about 3D molecular structures with varying size is an emerging and important challenge in machine learning and especially in drug discovery. Equivariant Graph Neural Networks (GNNs) can simultaneously leverage the geometric and relational detail of the problem domain and are known to learn expressive representations through the propagation of information between nodes leveraging higher-order representations to faithfully express the geometry of the data, such as directionality in their intermediate layers. In this work, we propose an equivariant GNN that operates with Ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.09891","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2202.09891/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2202.09891","created_at":"2026-07-05T04:01:34.700748+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.09891v2","created_at":"2026-07-05T04:01:34.700748+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.09891","created_at":"2026-07-05T04:01:34.700748+00:00"},{"alias_kind":"pith_short_12","alias_value":"FGRSS5ZQQHQL","created_at":"2026-07-05T04:01:34.700748+00:00"},{"alias_kind":"pith_short_16","alias_value":"FGRSS5ZQQHQLC7KK","created_at":"2026-07-05T04:01:34.700748+00:00"},{"alias_kind":"pith_short_8","alias_value":"FGRSS5ZQ","created_at":"2026-07-05T04:01:34.700748+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.11082","citing_title":"EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport for 3D Molecular Conformation Prediction","ref_index":15,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI","json":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI.json","graph_json":"https://pith.science/api/pith-number/FGRSS5ZQQHQLC7KKRSZC5NVAZI/graph.json","events_json":"https://pith.science/api/pith-number/FGRSS5ZQQHQLC7KKRSZC5NVAZI/events.json","paper":"https://pith.science/paper/FGRSS5ZQ"},"agent_actions":{"view_html":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI","download_json":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI.json","view_paper":"https://pith.science/paper/FGRSS5ZQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.09891&json=true","fetch_graph":"https://pith.science/api/pith-number/FGRSS5ZQQHQLC7KKRSZC5NVAZI/graph.json","fetch_events":"https://pith.science/api/pith-number/FGRSS5ZQQHQLC7KKRSZC5NVAZI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI/action/storage_attestation","attest_author":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI/action/author_attestation","sign_citation":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI/action/citation_signature","submit_replication":"https://pith.science/pith/FGRSS5ZQQHQLC7KKRSZC5NVAZI/action/replication_record"}},"created_at":"2026-07-05T04:01:34.700748+00:00","updated_at":"2026-07-05T04:01:34.700748+00:00"}