{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:4RBQHOUE4RT5ZSQ3EYHWTM3EBP","short_pith_number":"pith:4RBQHOUE","schema_version":"1.0","canonical_sha256":"e44303ba84e467dcca1b260f69b3640bd3b5e2b62e0f8155b676ebb3bd68e632","source":{"kind":"arxiv","id":"2405.20668","version":1},"attestation_state":"computed","paper":{"title":"Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"q-bio.BM","authors_text":"Wen Zhang, Yongkang Wang, Zhiwei Wang","submitted_at":"2024-05-31T08:09:36Z","abstract_excerpt":"Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure dat"},"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":"2405.20668","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2024-05-31T08:09:36Z","cross_cats_sorted":["cs.LG","q-bio.QM"],"title_canon_sha256":"ac869607db2941dec1f891070b394b6973e600cf4a4dd0f7cf0eebe875da1d20","abstract_canon_sha256":"1e7778eab6e8c2d71ef212df77c83433ee7ec2d021bfc16d712827a745972e80"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:25:40.688433Z","signature_b64":"4XnDAx7MtlAZWSjJirllGgSTLvD3vnAhYc0+PZHynPN9cfxtmywoTd4K97rd2yFtxXRXaB5H/AjNYJ+YKuaUAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e44303ba84e467dcca1b260f69b3640bd3b5e2b62e0f8155b676ebb3bd68e632","last_reissued_at":"2026-07-05T08:25:40.687907Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:25:40.687907Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improving Paratope and Epitope Prediction by Multi-Modal Contrastive Learning and Interaction Informativeness Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","q-bio.QM"],"primary_cat":"q-bio.BM","authors_text":"Wen Zhang, Yongkang Wang, Zhiwei Wang","submitted_at":"2024-05-31T08:09:36Z","abstract_excerpt":"Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure dat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.20668","kind":"arxiv","version":1},"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/2405.20668/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":"2405.20668","created_at":"2026-07-05T08:25:40.687965+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.20668v1","created_at":"2026-07-05T08:25:40.687965+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.20668","created_at":"2026-07-05T08:25:40.687965+00:00"},{"alias_kind":"pith_short_12","alias_value":"4RBQHOUE4RT5","created_at":"2026-07-05T08:25:40.687965+00:00"},{"alias_kind":"pith_short_16","alias_value":"4RBQHOUE4RT5ZSQ3","created_at":"2026-07-05T08:25:40.687965+00:00"},{"alias_kind":"pith_short_8","alias_value":"4RBQHOUE","created_at":"2026-07-05T08:25:40.687965+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04154","citing_title":"EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning","ref_index":147,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21600","citing_title":"ConTact: Contact-First Antibody CDR Design via Explicit Interface Reasoning","ref_index":172,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21610","citing_title":"AgForce Enables Antigen-conditioned Generative Antibody Design","ref_index":172,"is_internal_anchor":false},{"citing_arxiv_id":"2605.21485","citing_title":"EvoStruct: Bridging Evolutionary and Structural Priors for Antibody CDR Design via Protein Language Model Adaptation","ref_index":172,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP","json":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP.json","graph_json":"https://pith.science/api/pith-number/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/graph.json","events_json":"https://pith.science/api/pith-number/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/events.json","paper":"https://pith.science/paper/4RBQHOUE"},"agent_actions":{"view_html":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP","download_json":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP.json","view_paper":"https://pith.science/paper/4RBQHOUE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.20668&json=true","fetch_graph":"https://pith.science/api/pith-number/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/graph.json","fetch_events":"https://pith.science/api/pith-number/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/action/storage_attestation","attest_author":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/action/author_attestation","sign_citation":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/action/citation_signature","submit_replication":"https://pith.science/pith/4RBQHOUE4RT5ZSQ3EYHWTM3EBP/action/replication_record"}},"created_at":"2026-07-05T08:25:40.687965+00:00","updated_at":"2026-07-05T08:25:40.687965+00:00"}