{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:TTC2ALTJ3QXWELY2LTKDPTIPMV","short_pith_number":"pith:TTC2ALTJ","schema_version":"1.0","canonical_sha256":"9cc5a02e69dc2f622f1a5cd437cd0f6555527d16c04a87f4d5cfeb86a62b961e","source":{"kind":"arxiv","id":"2209.15611","version":2},"attestation_state":"computed","paper":{"title":"Protein structure generation via folding diffusion","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.BM","authors_text":"Alex X. Lu, Ava P. Amini, James Y. Zou, Kevin E. Wu, Kevin K. Yang, Rianne van den Berg","submitted_at":"2022-09-30T17:35:53Z","abstract_excerpt":"The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientat"},"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":"2209.15611","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"q-bio.BM","submitted_at":"2022-09-30T17:35:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"13d0b51f30abbf0147d1af828f68fc40378601f6b1a93118adaf22e1aa619d75","abstract_canon_sha256":"91d41bd39ee54e82941d777fcd63748990738be632324cbcc3b6fa90dd1be82e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:19:01.184993Z","signature_b64":"jm1xCUKNhGHeoc0n414ib3T+ZobTQFTZ2huGAfSfrRpB9F0rl05d2t55IBYdk8PMiRzKluDaxy0CXtNAFRswDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9cc5a02e69dc2f622f1a5cd437cd0f6555527d16c04a87f4d5cfeb86a62b961e","last_reissued_at":"2026-07-05T05:19:01.184581Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:19:01.184581Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Protein structure generation via folding diffusion","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"q-bio.BM","authors_text":"Alex X. Lu, Ava P. Amini, James Y. Zou, Kevin E. Wu, Kevin K. Yang, Rianne van den Berg","submitted_at":"2022-09-30T17:35:53Z","abstract_excerpt":"The ability to computationally generate novel yet physically foldable protein structures could lead to new biological discoveries and new treatments targeting yet incurable diseases. Despite recent advances in protein structure prediction, directly generating diverse, novel protein structures from neural networks remains difficult. In this work, we present a new diffusion-based generative model that designs protein backbone structures via a procedure that mirrors the native folding process. We describe protein backbone structure as a series of consecutive angles capturing the relative orientat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2209.15611","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/2209.15611/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":"2209.15611","created_at":"2026-07-05T05:19:01.184635+00:00"},{"alias_kind":"arxiv_version","alias_value":"2209.15611v2","created_at":"2026-07-05T05:19:01.184635+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2209.15611","created_at":"2026-07-05T05:19:01.184635+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTC2ALTJ3QXW","created_at":"2026-07-05T05:19:01.184635+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTC2ALTJ3QXWELY2","created_at":"2026-07-05T05:19:01.184635+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTC2ALTJ","created_at":"2026-07-05T05:19:01.184635+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2211.01324","citing_title":"eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers","ref_index":84,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV","json":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV.json","graph_json":"https://pith.science/api/pith-number/TTC2ALTJ3QXWELY2LTKDPTIPMV/graph.json","events_json":"https://pith.science/api/pith-number/TTC2ALTJ3QXWELY2LTKDPTIPMV/events.json","paper":"https://pith.science/paper/TTC2ALTJ"},"agent_actions":{"view_html":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV","download_json":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV.json","view_paper":"https://pith.science/paper/TTC2ALTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2209.15611&json=true","fetch_graph":"https://pith.science/api/pith-number/TTC2ALTJ3QXWELY2LTKDPTIPMV/graph.json","fetch_events":"https://pith.science/api/pith-number/TTC2ALTJ3QXWELY2LTKDPTIPMV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV/action/storage_attestation","attest_author":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV/action/author_attestation","sign_citation":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV/action/citation_signature","submit_replication":"https://pith.science/pith/TTC2ALTJ3QXWELY2LTKDPTIPMV/action/replication_record"}},"created_at":"2026-07-05T05:19:01.184635+00:00","updated_at":"2026-07-05T05:19:01.184635+00:00"}