{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:M24EJXFWCQ4SMEMSIHCT3G4VR7","short_pith_number":"pith:M24EJXFW","schema_version":"1.0","canonical_sha256":"66b844dcb6143926119241c53d9b958fde9c59c073006c39c692760f25101b62","source":{"kind":"arxiv","id":"2605.29108","version":1},"attestation_state":"computed","paper":{"title":"Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Giulia Bergonzini, Marco V. Mijangos, Mikhail Kabeshov, Ola Engkvist, Samuel Genheden, Samuel Kaski, Tat Hong Duong Le, Varvara Voinarvoska, Yujia Guo","submitted_at":"2026-05-27T21:15:51Z","abstract_excerpt":"Selecting efficient multi-step synthetic routes is a central challenge in organic synthesis, particularly in medicinal and process chemistry, where route choice directly impacts feasibility, cost, and development efficiency. Data-driven assessment systems often oversimplify the multi-objective nature of synthesis design and rely on proxy datasets, such as patent routes, rather than universally grounded criteria. To address this, we introduce an expert-augmented, data-driven scoring framework that integrates machine learning with chemists' domain knowledge for both numerical and explainable rou"},"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":"2605.29108","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-27T21:15:51Z","cross_cats_sorted":[],"title_canon_sha256":"51a4f591675b6618723255ea74fb17dd58b19548696ec9cf95973b5cb6a564a8","abstract_canon_sha256":"c455fca04b9b388bfa68cb06a54c3edd30579bd343b145b39eb2c987be1665b3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:18.662776Z","signature_b64":"bkepZo5ePglJ06pNVPD/qV2TfKYOGZHQKGT2ngPh0Om0Tki9PB33AUqdBi3ME3tbRaPIpKEHmuA6zHptq8IIDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"66b844dcb6143926119241c53d9b958fde9c59c073006c39c692760f25101b62","last_reissued_at":"2026-05-29T01:05:18.661959Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:18.661959Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Giulia Bergonzini, Marco V. Mijangos, Mikhail Kabeshov, Ola Engkvist, Samuel Genheden, Samuel Kaski, Tat Hong Duong Le, Varvara Voinarvoska, Yujia Guo","submitted_at":"2026-05-27T21:15:51Z","abstract_excerpt":"Selecting efficient multi-step synthetic routes is a central challenge in organic synthesis, particularly in medicinal and process chemistry, where route choice directly impacts feasibility, cost, and development efficiency. Data-driven assessment systems often oversimplify the multi-objective nature of synthesis design and rely on proxy datasets, such as patent routes, rather than universally grounded criteria. To address this, we introduce an expert-augmented, data-driven scoring framework that integrates machine learning with chemists' domain knowledge for both numerical and explainable rou"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29108","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/2605.29108/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":"2605.29108","created_at":"2026-05-29T01:05:18.662109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29108v1","created_at":"2026-05-29T01:05:18.662109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29108","created_at":"2026-05-29T01:05:18.662109+00:00"},{"alias_kind":"pith_short_12","alias_value":"M24EJXFWCQ4S","created_at":"2026-05-29T01:05:18.662109+00:00"},{"alias_kind":"pith_short_16","alias_value":"M24EJXFWCQ4SMEMS","created_at":"2026-05-29T01:05:18.662109+00:00"},{"alias_kind":"pith_short_8","alias_value":"M24EJXFW","created_at":"2026-05-29T01:05:18.662109+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/M24EJXFWCQ4SMEMSIHCT3G4VR7","json":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7.json","graph_json":"https://pith.science/api/pith-number/M24EJXFWCQ4SMEMSIHCT3G4VR7/graph.json","events_json":"https://pith.science/api/pith-number/M24EJXFWCQ4SMEMSIHCT3G4VR7/events.json","paper":"https://pith.science/paper/M24EJXFW"},"agent_actions":{"view_html":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7","download_json":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7.json","view_paper":"https://pith.science/paper/M24EJXFW","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29108&json=true","fetch_graph":"https://pith.science/api/pith-number/M24EJXFWCQ4SMEMSIHCT3G4VR7/graph.json","fetch_events":"https://pith.science/api/pith-number/M24EJXFWCQ4SMEMSIHCT3G4VR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7/action/storage_attestation","attest_author":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7/action/author_attestation","sign_citation":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7/action/citation_signature","submit_replication":"https://pith.science/pith/M24EJXFWCQ4SMEMSIHCT3G4VR7/action/replication_record"}},"created_at":"2026-05-29T01:05:18.662109+00:00","updated_at":"2026-05-29T01:05:18.662109+00:00"}