{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:DZES53XJU2FGCOICFMAGUFZZRB","short_pith_number":"pith:DZES53XJ","schema_version":"1.0","canonical_sha256":"1e492eeee9a68a6139022b006a17398857b5405d84d10f31ddfa9c8f7e458e79","source":{"kind":"arxiv","id":"2302.00203","version":4},"attestation_state":"computed","paper":{"title":"End-to-End Full-Atom Antibody Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Wenbing Huang, Xiangzhe Kong, Yang Liu","submitted_at":"2023-02-01T03:13:19Z","abstract_excerpt":"Antibody design is an essential yet challenging task in various domains like therapeutics and biology. There are two major defects in current learning-based methods: 1) tackling only a certain subtask of the whole antibody design pipeline, making them suboptimal or resource-intensive. 2) omitting either the framework regions or side chains, thus incapable of capturing the full-atom geometry. To address these pitfalls, we propose dynamic Multi-channel Equivariant grAph Network (dyMEAN), an end-to-end full-atom model for E(3)-equivariant antibody design given the epitope and the incomplete seque"},"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":"2302.00203","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2023-02-01T03:13:19Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"a0be7082ddab926aec80f052856c68e616affc9ba3e3dd138c7c3ac717016a19","abstract_canon_sha256":"267d6843daf9b95b88fdbd083358977a714e47375db46ec50770e0029ff4567f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:14:57.330168Z","signature_b64":"DnYrGKbeyUSxaqU+lU95Alto9JHzHFweaI8gkYEiYImXG8vKF/uIWrfON4VSJzt8itjggInx3rrR+e+x7OuxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1e492eeee9a68a6139022b006a17398857b5405d84d10f31ddfa9c8f7e458e79","last_reissued_at":"2026-07-05T06:14:57.329684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:14:57.329684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End-to-End Full-Atom Antibody Design","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Wenbing Huang, Xiangzhe Kong, Yang Liu","submitted_at":"2023-02-01T03:13:19Z","abstract_excerpt":"Antibody design is an essential yet challenging task in various domains like therapeutics and biology. There are two major defects in current learning-based methods: 1) tackling only a certain subtask of the whole antibody design pipeline, making them suboptimal or resource-intensive. 2) omitting either the framework regions or side chains, thus incapable of capturing the full-atom geometry. To address these pitfalls, we propose dynamic Multi-channel Equivariant grAph Network (dyMEAN), an end-to-end full-atom model for E(3)-equivariant antibody design given the epitope and the incomplete seque"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.00203","kind":"arxiv","version":4},"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/2302.00203/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":"2302.00203","created_at":"2026-07-05T06:14:57.329744+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.00203v4","created_at":"2026-07-05T06:14:57.329744+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.00203","created_at":"2026-07-05T06:14:57.329744+00:00"},{"alias_kind":"pith_short_12","alias_value":"DZES53XJU2FG","created_at":"2026-07-05T06:14:57.329744+00:00"},{"alias_kind":"pith_short_16","alias_value":"DZES53XJU2FGCOIC","created_at":"2026-07-05T06:14:57.329744+00:00"},{"alias_kind":"pith_short_8","alias_value":"DZES53XJ","created_at":"2026-07-05T06:14:57.329744+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12991","citing_title":"APCyc: Property-Informed Design of Cyclic Peptides via Automated Cyclization","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28886","citing_title":"Computational Modeling of Antibody-Antigen Complexes: PLM-Based and MSA-Based Approaches","ref_index":111,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB","json":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB.json","graph_json":"https://pith.science/api/pith-number/DZES53XJU2FGCOICFMAGUFZZRB/graph.json","events_json":"https://pith.science/api/pith-number/DZES53XJU2FGCOICFMAGUFZZRB/events.json","paper":"https://pith.science/paper/DZES53XJ"},"agent_actions":{"view_html":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB","download_json":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB.json","view_paper":"https://pith.science/paper/DZES53XJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.00203&json=true","fetch_graph":"https://pith.science/api/pith-number/DZES53XJU2FGCOICFMAGUFZZRB/graph.json","fetch_events":"https://pith.science/api/pith-number/DZES53XJU2FGCOICFMAGUFZZRB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB/action/storage_attestation","attest_author":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB/action/author_attestation","sign_citation":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB/action/citation_signature","submit_replication":"https://pith.science/pith/DZES53XJU2FGCOICFMAGUFZZRB/action/replication_record"}},"created_at":"2026-07-05T06:14:57.329744+00:00","updated_at":"2026-07-05T06:14:57.329744+00:00"}