{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7AWMWFB6ALSQUWLSMT4AVQFXIL","short_pith_number":"pith:7AWMWFB6","schema_version":"1.0","canonical_sha256":"f82ccb143e02e50a597264f80ac0b742c338ebc4e2395353cf98e24dbd871f46","source":{"kind":"arxiv","id":"2508.01459","version":1},"attestation_state":"computed","paper":{"title":"Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Djork-Arn\\'e Clevert, J\\\"urgen Schmidhuber, Michael Wand, Mikhail Andronov, Natalia Andronova","submitted_at":"2025-08-02T18:30:06Z","abstract_excerpt":"AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models. Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable d"},"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":"2508.01459","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2025-08-02T18:30:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b8afab5c3d39c745f365167011c7fcda060755e78fd51120c6b47a64bc5249ad","abstract_canon_sha256":"53bbd4c0579665427460b19af801e2be1f4970ff9c2f36c6058507531fa6fa8d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:47:39.346623Z","signature_b64":"5Z2VbdF+AgOA5K5v5QaMO2nZ5KWr7d5SwPsOmLvQbgg5p6uCRa5RGCsbnV9ZWwoBu9jjvGeBey5tL7QC+mMvCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f82ccb143e02e50a597264f80ac0b742c338ebc4e2395353cf98e24dbd871f46","last_reissued_at":"2026-07-05T11:47:39.346050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:47:39.346050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Djork-Arn\\'e Clevert, J\\\"urgen Schmidhuber, Michael Wand, Mikhail Andronov, Natalia Andronova","submitted_at":"2025-08-02T18:30:06Z","abstract_excerpt":"AI-based computer-aided synthesis planning (CASP) systems are in demand as components of AI-driven drug discovery workflows. However, the high latency of such CASP systems limits their utility for high-throughput synthesizability screening in de novo drug design. We propose a method for accelerating multi-step synthesis planning systems that rely on SMILES-to-SMILES transformers as single-step retrosynthesis models. Our approach reduces the latency of SMILES-to-SMILES transformers powering multi-step synthesis planning in AiZynthFinder through speculative beam search combined with a scalable d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.01459","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/2508.01459/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":"2508.01459","created_at":"2026-07-05T11:47:39.346109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.01459v1","created_at":"2026-07-05T11:47:39.346109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.01459","created_at":"2026-07-05T11:47:39.346109+00:00"},{"alias_kind":"pith_short_12","alias_value":"7AWMWFB6ALSQ","created_at":"2026-07-05T11:47:39.346109+00:00"},{"alias_kind":"pith_short_16","alias_value":"7AWMWFB6ALSQUWLS","created_at":"2026-07-05T11:47:39.346109+00:00"},{"alias_kind":"pith_short_8","alias_value":"7AWMWFB6","created_at":"2026-07-05T11:47:39.346109+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL","json":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL.json","graph_json":"https://pith.science/api/pith-number/7AWMWFB6ALSQUWLSMT4AVQFXIL/graph.json","events_json":"https://pith.science/api/pith-number/7AWMWFB6ALSQUWLSMT4AVQFXIL/events.json","paper":"https://pith.science/paper/7AWMWFB6"},"agent_actions":{"view_html":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL","download_json":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL.json","view_paper":"https://pith.science/paper/7AWMWFB6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.01459&json=true","fetch_graph":"https://pith.science/api/pith-number/7AWMWFB6ALSQUWLSMT4AVQFXIL/graph.json","fetch_events":"https://pith.science/api/pith-number/7AWMWFB6ALSQUWLSMT4AVQFXIL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL/action/storage_attestation","attest_author":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL/action/author_attestation","sign_citation":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL/action/citation_signature","submit_replication":"https://pith.science/pith/7AWMWFB6ALSQUWLSMT4AVQFXIL/action/replication_record"}},"created_at":"2026-07-05T11:47:39.346109+00:00","updated_at":"2026-07-05T11:47:39.346109+00:00"}