{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:MGQDS6D2LG66OV3SW6BPTND3F3","short_pith_number":"pith:MGQDS6D2","schema_version":"1.0","canonical_sha256":"61a039787a59bde75772b782f9b47b2ed8b9b8ac3da1c1bcc37cf2a68d308be7","source":{"kind":"arxiv","id":"2312.09236","version":4},"attestation_state":"computed","paper":{"title":"A framework for conditional diffusion modelling with applications in motif scaffolding for protein design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Emile Mathieu, Francisco Vargas, Kieran Didi, Pietro Lio, Simon V Mathis, Urszula J Komorowska, Vincent Dutordoir","submitted_at":"2023-12-14T18:57:56Z","abstract_excerpt":"Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature. However, most of these protocols are motivated heuristically, e.g. via anal"},"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":"2312.09236","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-14T18:57:56Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"cff36ef4f05afefe7519dc829708b7311bd78b4641c5a6d982cd31803a1a3011","abstract_canon_sha256":"3f126973ffc465ef8055868d85e42cdfe4c473f5dd0d9e8eff68630ce4d32089"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:55:46.056446Z","signature_b64":"+4hD1Z/HwhRoqTjU51QUAiK5/gcs7atEC8HK0DVqMnSlx/OsD67enBreJwgvpUS0dJtXVPidQGDPVlFoCBibAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61a039787a59bde75772b782f9b47b2ed8b9b8ac3da1c1bcc37cf2a68d308be7","last_reissued_at":"2026-07-05T07:55:46.055977Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:55:46.055977Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A framework for conditional diffusion modelling with applications in motif scaffolding for protein design","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Emile Mathieu, Francisco Vargas, Kieran Didi, Pietro Lio, Simon V Mathis, Urszula J Komorowska, Vincent Dutordoir","submitted_at":"2023-12-14T18:57:56Z","abstract_excerpt":"Many protein design applications, such as binder or enzyme design, require scaffolding a structural motif with high precision. Generative modelling paradigms based on denoising diffusion processes emerged as a leading candidate to address this motif scaffolding problem and have shown early experimental success in some cases. In the diffusion paradigm, motif scaffolding is treated as a conditional generation task, and several conditional generation protocols were proposed or imported from the Computer Vision literature. However, most of these protocols are motivated heuristically, e.g. via anal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.09236","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/2312.09236/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":"2312.09236","created_at":"2026-07-05T07:55:46.056049+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.09236v4","created_at":"2026-07-05T07:55:46.056049+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.09236","created_at":"2026-07-05T07:55:46.056049+00:00"},{"alias_kind":"pith_short_12","alias_value":"MGQDS6D2LG66","created_at":"2026-07-05T07:55:46.056049+00:00"},{"alias_kind":"pith_short_16","alias_value":"MGQDS6D2LG66OV3S","created_at":"2026-07-05T07:55:46.056049+00:00"},{"alias_kind":"pith_short_8","alias_value":"MGQDS6D2","created_at":"2026-07-05T07:55:46.056049+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.12710","citing_title":"A Stabilized Path-Space Approach to Diffusion-Based Posterior Sampling","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05387","citing_title":"Conditional Diffusion Under Linear Constraints: Langevin Mixing and Information-Theoretic Guarantees","ref_index":4,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3","json":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3.json","graph_json":"https://pith.science/api/pith-number/MGQDS6D2LG66OV3SW6BPTND3F3/graph.json","events_json":"https://pith.science/api/pith-number/MGQDS6D2LG66OV3SW6BPTND3F3/events.json","paper":"https://pith.science/paper/MGQDS6D2"},"agent_actions":{"view_html":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3","download_json":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3.json","view_paper":"https://pith.science/paper/MGQDS6D2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.09236&json=true","fetch_graph":"https://pith.science/api/pith-number/MGQDS6D2LG66OV3SW6BPTND3F3/graph.json","fetch_events":"https://pith.science/api/pith-number/MGQDS6D2LG66OV3SW6BPTND3F3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3/action/storage_attestation","attest_author":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3/action/author_attestation","sign_citation":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3/action/citation_signature","submit_replication":"https://pith.science/pith/MGQDS6D2LG66OV3SW6BPTND3F3/action/replication_record"}},"created_at":"2026-07-05T07:55:46.056049+00:00","updated_at":"2026-07-05T07:55:46.056049+00:00"}