{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:NXY56PX4XQRK66E2MGATTSWYFW","short_pith_number":"pith:NXY56PX4","schema_version":"1.0","canonical_sha256":"6df1df3efcbc22af789a618139cad82dbac009183922b00843705ca451f7bbb4","source":{"kind":"arxiv","id":"2301.00427","version":2},"attestation_state":"computed","paper":{"title":"Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Bowen Du, Han Huang, Leilei Sun, Weifeng Lv","submitted_at":"2023-01-01T15:24:15Z","abstract_excerpt":"Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) an"},"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":"2301.00427","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2023-01-01T15:24:15Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"68a07e23eeb2d95aae7afeb2f58c6d83255c229082c3ef0e41ee2ce2fd089ccc","abstract_canon_sha256":"6ce3ebe1d62d70583d426f2a00972123fdd180e7ad11d3632943ce8040f348a5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:12:41.553115Z","signature_b64":"Os2AKpyLhDET5KcYooyJ1t9q3sL78F/YaDEAYr9heKx9IIR/wlF8n1LbKvzvngSF8BQIr5w7LtoMzHG6Ggo8Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6df1df3efcbc22af789a618139cad82dbac009183922b00843705ca451f7bbb4","last_reissued_at":"2026-07-05T06:12:41.552564Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:12:41.552564Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Conditional Diffusion Based on Discrete Graph Structures for Molecular Graph Generation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Bowen Du, Han Huang, Leilei Sun, Weifeng Lv","submitted_at":"2023-01-01T15:24:15Z","abstract_excerpt":"Learning the underlying distribution of molecular graphs and generating high-fidelity samples is a fundamental research problem in drug discovery and material science. However, accurately modeling distribution and rapidly generating novel molecular graphs remain crucial and challenging goals. To accomplish these goals, we propose a novel Conditional Diffusion model based on discrete Graph Structures (CDGS) for molecular graph generation. Specifically, we construct a forward graph diffusion process on both graph structures and inherent features through stochastic differential equations (SDE) an"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.00427","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/2301.00427/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":"2301.00427","created_at":"2026-07-05T06:12:41.552643+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.00427v2","created_at":"2026-07-05T06:12:41.552643+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.00427","created_at":"2026-07-05T06:12:41.552643+00:00"},{"alias_kind":"pith_short_12","alias_value":"NXY56PX4XQRK","created_at":"2026-07-05T06:12:41.552643+00:00"},{"alias_kind":"pith_short_16","alias_value":"NXY56PX4XQRK66E2","created_at":"2026-07-05T06:12:41.552643+00:00"},{"alias_kind":"pith_short_8","alias_value":"NXY56PX4","created_at":"2026-07-05T06:12:41.552643+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.16365","citing_title":"A collaborative constrained graph diffusion model for the generation of realistic synthetic molecules","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW","json":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW.json","graph_json":"https://pith.science/api/pith-number/NXY56PX4XQRK66E2MGATTSWYFW/graph.json","events_json":"https://pith.science/api/pith-number/NXY56PX4XQRK66E2MGATTSWYFW/events.json","paper":"https://pith.science/paper/NXY56PX4"},"agent_actions":{"view_html":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW","download_json":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW.json","view_paper":"https://pith.science/paper/NXY56PX4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.00427&json=true","fetch_graph":"https://pith.science/api/pith-number/NXY56PX4XQRK66E2MGATTSWYFW/graph.json","fetch_events":"https://pith.science/api/pith-number/NXY56PX4XQRK66E2MGATTSWYFW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW/action/storage_attestation","attest_author":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW/action/author_attestation","sign_citation":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW/action/citation_signature","submit_replication":"https://pith.science/pith/NXY56PX4XQRK66E2MGATTSWYFW/action/replication_record"}},"created_at":"2026-07-05T06:12:41.552643+00:00","updated_at":"2026-07-05T06:12:41.552643+00:00"}