{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:32RE7RV2VEDBSQBBFGXPJG7NO6","short_pith_number":"pith:32RE7RV2","schema_version":"1.0","canonical_sha256":"dea24fc6baa90619402129aef49bed7786336c9fe3532953cf2b4725c6c78fc5","source":{"kind":"arxiv","id":"2110.09681","version":1},"attestation_state":"computed","paper":{"title":"Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Connor W. Coley, Zhengkai Tu","submitted_at":"2021-10-19T01:23:15Z","abstract_excerpt":"Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not an efficient representation for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boo"},"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":"2110.09681","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-10-19T01:23:15Z","cross_cats_sorted":[],"title_canon_sha256":"ad55505e4ade9abcb440dd81933c7918e7b6d6295abf0b43e96186a4617261cc","abstract_canon_sha256":"daa93f0b01a81bfc916baf29131c764a8e4fa958c751533933af8d373d32d67c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:23:57.832325Z","signature_b64":"E+mXcbfy8ODdg+RxFPzdu8aaT2HqozANjEXwUqrIJ93YDiK8gX2MW0Ux8NLDC1eJP38d16sNXgHnjGpGzGbJCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dea24fc6baa90619402129aef49bed7786336c9fe3532953cf2b4725c6c78fc5","last_reissued_at":"2026-07-05T03:23:57.831799Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:23:57.831799Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Permutation invariant graph-to-sequence model for template-free retrosynthesis and reaction prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Connor W. Coley, Zhengkai Tu","submitted_at":"2021-10-19T01:23:15Z","abstract_excerpt":"Synthesis planning and reaction outcome prediction are two fundamental problems in computer-aided organic chemistry for which a variety of data-driven approaches have emerged. Natural language approaches that model each problem as a SMILES-to-SMILES translation lead to a simple end-to-end formulation, reduce the need for data preprocessing, and enable the use of well-optimized machine translation model architectures. However, SMILES representations are not an efficient representation for capturing information about molecular structures, as evidenced by the success of SMILES augmentation to boo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.09681","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/2110.09681/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":"2110.09681","created_at":"2026-07-05T03:23:57.831863+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.09681v1","created_at":"2026-07-05T03:23:57.831863+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.09681","created_at":"2026-07-05T03:23:57.831863+00:00"},{"alias_kind":"pith_short_12","alias_value":"32RE7RV2VEDB","created_at":"2026-07-05T03:23:57.831863+00:00"},{"alias_kind":"pith_short_16","alias_value":"32RE7RV2VEDBSQBB","created_at":"2026-07-05T03:23:57.831863+00:00"},{"alias_kind":"pith_short_8","alias_value":"32RE7RV2","created_at":"2026-07-05T03:23:57.831863+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2607.01061","citing_title":"Agentic generation of verifiable rules for deterministic, self-expanding reaction classification","ref_index":252,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6","json":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6.json","graph_json":"https://pith.science/api/pith-number/32RE7RV2VEDBSQBBFGXPJG7NO6/graph.json","events_json":"https://pith.science/api/pith-number/32RE7RV2VEDBSQBBFGXPJG7NO6/events.json","paper":"https://pith.science/paper/32RE7RV2"},"agent_actions":{"view_html":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6","download_json":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6.json","view_paper":"https://pith.science/paper/32RE7RV2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.09681&json=true","fetch_graph":"https://pith.science/api/pith-number/32RE7RV2VEDBSQBBFGXPJG7NO6/graph.json","fetch_events":"https://pith.science/api/pith-number/32RE7RV2VEDBSQBBFGXPJG7NO6/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6/action/timestamp_anchor","attest_storage":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6/action/storage_attestation","attest_author":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6/action/author_attestation","sign_citation":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6/action/citation_signature","submit_replication":"https://pith.science/pith/32RE7RV2VEDBSQBBFGXPJG7NO6/action/replication_record"}},"created_at":"2026-07-05T03:23:57.831863+00:00","updated_at":"2026-07-05T03:23:57.831863+00:00"}