{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:6XE5JZOLQZ357KO3DP2O5CYLRL","short_pith_number":"pith:6XE5JZOL","schema_version":"1.0","canonical_sha256":"f5c9d4e5cb8677dfa9db1bf4ee8b0b8acad7a482964d1734a00b2661b525bff3","source":{"kind":"arxiv","id":"2110.04624","version":3},"attestation_state":"computed","paper":{"title":"Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola, Wengong Jin","submitted_at":"2021-10-09T18:23:32Z","abstract_excerpt":"Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-de"},"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":"2110.04624","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2021-10-09T18:23:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a80a38b8c5b2372a89b371b69da1983b2474014119974c6e6f696d9cfad3fa3c","abstract_canon_sha256":"e5181ff9916bcf7c2bf00348deb4043e45e23773497ca199af347def33d90588"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:52:14.884237Z","signature_b64":"XRQzDNcSJto8+HLKcLj3jlbAN8XPxcQCIGnRbW20XsY24JIT2hTHMW90GGdWs21J9ysP+ZiOYqANrwkTMiBBDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f5c9d4e5cb8677dfa9db1bf4ee8b0b8acad7a482964d1734a00b2661b525bff3","last_reissued_at":"2026-07-05T03:52:14.883750Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:52:14.883750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Jeremy Wohlwend, Regina Barzilay, Tommi Jaakkola, Wengong Jin","submitted_at":"2021-10-09T18:23:32Z","abstract_excerpt":"Antibodies are versatile proteins that bind to pathogens like viruses and stimulate the adaptive immune system. The specificity of antibody binding is determined by complementarity-determining regions (CDRs) at the tips of these Y-shaped proteins. In this paper, we propose a generative model to automatically design the CDRs of antibodies with enhanced binding specificity or neutralization capabilities. Previous generative approaches formulate protein design as a structure-conditioned sequence generation task, assuming the desired 3D structure is given a priori. In contrast, we propose to co-de"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.04624","kind":"arxiv","version":3},"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/2110.04624/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":"2110.04624","created_at":"2026-07-05T03:52:14.883809+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.04624v3","created_at":"2026-07-05T03:52:14.883809+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.04624","created_at":"2026-07-05T03:52:14.883809+00:00"},{"alias_kind":"pith_short_12","alias_value":"6XE5JZOLQZ35","created_at":"2026-07-05T03:52:14.883809+00:00"},{"alias_kind":"pith_short_16","alias_value":"6XE5JZOLQZ357KO3","created_at":"2026-07-05T03:52:14.883809+00:00"},{"alias_kind":"pith_short_8","alias_value":"6XE5JZOL","created_at":"2026-07-05T03:52:14.883809+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.20957","citing_title":"Antibody Design and Optimization with Multi-scale Equivariant Graph Diffusion Models for Accurate Complex Antigen Binding","ref_index":12,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL","json":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL.json","graph_json":"https://pith.science/api/pith-number/6XE5JZOLQZ357KO3DP2O5CYLRL/graph.json","events_json":"https://pith.science/api/pith-number/6XE5JZOLQZ357KO3DP2O5CYLRL/events.json","paper":"https://pith.science/paper/6XE5JZOL"},"agent_actions":{"view_html":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL","download_json":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL.json","view_paper":"https://pith.science/paper/6XE5JZOL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.04624&json=true","fetch_graph":"https://pith.science/api/pith-number/6XE5JZOLQZ357KO3DP2O5CYLRL/graph.json","fetch_events":"https://pith.science/api/pith-number/6XE5JZOLQZ357KO3DP2O5CYLRL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL/action/storage_attestation","attest_author":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL/action/author_attestation","sign_citation":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL/action/citation_signature","submit_replication":"https://pith.science/pith/6XE5JZOLQZ357KO3DP2O5CYLRL/action/replication_record"}},"created_at":"2026-07-05T03:52:14.883809+00:00","updated_at":"2026-07-05T03:52:14.883809+00:00"}