{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:U3TGMXYI43SKYEB366IJRJ4FGU","short_pith_number":"pith:U3TGMXYI","schema_version":"1.0","canonical_sha256":"a6e6665f08e6e4ac103bf79098a7853528f114b468950f7f72d131b94b375b8a","source":{"kind":"arxiv","id":"2410.06502","version":2},"attestation_state":"computed","paper":{"title":"Chemistry-Inspired Diffusion with Non-Differentiable Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Barnab\\'as P\\'oczos, Chenghui Zhou, Chenhao Zhang, Newell Washburn, Sijie Fu, Yuchen Shen","submitted_at":"2024-10-09T03:10:21Z","abstract_excerpt":"Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the condition, or (ii) implicitly, using a property predictor. However, training property predictors or conditional diffusion models requires an abundance of labeled data and is inherently challenging in real-world applications. We propose a novel approach that attenuates the limitations of acquiring large labeled datasets by leveraging domain knowledge from quantum chemistry as a n"},"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":"2410.06502","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-10-09T03:10:21Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"cc451f02c0b3c000222dcbe853ae31b3e27ee8f68dcec5232c517be066ea30ed","abstract_canon_sha256":"8e8523d285ef26da18ecee4c75c20ec931565dd983c32c5dc9f2dbf4e3540bc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:28:23.989619Z","signature_b64":"OWTi++AOPyDt1aj42jYfC9W483l8B6N8FCFNiFhGQ+JAn65RBE3gAkAtyP9GxQlRpOtJLyqkrYgNfbW5RCA0Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6e6665f08e6e4ac103bf79098a7853528f114b468950f7f72d131b94b375b8a","last_reissued_at":"2026-07-05T10:28:23.989164Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:28:23.989164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Chemistry-Inspired Diffusion with Non-Differentiable Guidance","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Barnab\\'as P\\'oczos, Chenghui Zhou, Chenhao Zhang, Newell Washburn, Sijie Fu, Yuchen Shen","submitted_at":"2024-10-09T03:10:21Z","abstract_excerpt":"Recent advances in diffusion models have shown remarkable potential in the conditional generation of novel molecules. These models can be guided in two ways: (i) explicitly, through additional features representing the condition, or (ii) implicitly, using a property predictor. However, training property predictors or conditional diffusion models requires an abundance of labeled data and is inherently challenging in real-world applications. We propose a novel approach that attenuates the limitations of acquiring large labeled datasets by leveraging domain knowledge from quantum chemistry as a n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.06502","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/2410.06502/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":"2410.06502","created_at":"2026-07-05T10:28:23.989222+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.06502v2","created_at":"2026-07-05T10:28:23.989222+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.06502","created_at":"2026-07-05T10:28:23.989222+00:00"},{"alias_kind":"pith_short_12","alias_value":"U3TGMXYI43SK","created_at":"2026-07-05T10:28:23.989222+00:00"},{"alias_kind":"pith_short_16","alias_value":"U3TGMXYI43SKYEB3","created_at":"2026-07-05T10:28:23.989222+00:00"},{"alias_kind":"pith_short_8","alias_value":"U3TGMXYI","created_at":"2026-07-05T10:28:23.989222+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.02834","citing_title":"Learning from B Cell Evolution: Adaptive Multi-Expert Diffusion for Antibody Design via Online Optimization","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU","json":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU.json","graph_json":"https://pith.science/api/pith-number/U3TGMXYI43SKYEB366IJRJ4FGU/graph.json","events_json":"https://pith.science/api/pith-number/U3TGMXYI43SKYEB366IJRJ4FGU/events.json","paper":"https://pith.science/paper/U3TGMXYI"},"agent_actions":{"view_html":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU","download_json":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU.json","view_paper":"https://pith.science/paper/U3TGMXYI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.06502&json=true","fetch_graph":"https://pith.science/api/pith-number/U3TGMXYI43SKYEB366IJRJ4FGU/graph.json","fetch_events":"https://pith.science/api/pith-number/U3TGMXYI43SKYEB366IJRJ4FGU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU/action/storage_attestation","attest_author":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU/action/author_attestation","sign_citation":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU/action/citation_signature","submit_replication":"https://pith.science/pith/U3TGMXYI43SKYEB366IJRJ4FGU/action/replication_record"}},"created_at":"2026-07-05T10:28:23.989222+00:00","updated_at":"2026-07-05T10:28:23.989222+00:00"}