{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:LSOMMP7LWW3ROE672GCHBBSWY3","short_pith_number":"pith:LSOMMP7L","schema_version":"1.0","canonical_sha256":"5c9cc63febb5b71713dfd184708656c6cb6628c9b45745921abe680eb80b4fa1","source":{"kind":"arxiv","id":"2206.12411","version":2},"attestation_state":"computed","paper":{"title":"Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.CE","authors_text":"Connor W. Coley, Jimeng Sun, Tianfan Fu, Wenhao Gao","submitted_at":"2022-06-22T20:36:49Z","abstract_excerpt":"Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design. In recent years, significant progress has been made in solving challenging problems across various aspects of computational molecular optimizations, emphasizing high validity, diversity, and, most recently, synthesizability. Despite this progress, many papers report results on trivial or self-designed tasks, bringing additional challenges to directly assessing the performance of new methods. Moreover, the sample efficiency of the optimization--the number of molecules ev"},"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":"2206.12411","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CE","submitted_at":"2022-06-22T20:36:49Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"ca36ced5ff56c24afece8d6e1fcdeffc3525eb167e54ce1eb348d6dfd314cf3d","abstract_canon_sha256":"b0bf0026946f5d5e19089cf5a149b4e45a5203303aadda07609e2efa2a592c38"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:04:49.598724Z","signature_b64":"cAWlchmBm1uySWBgy1SEVZSjK+ZEpzSZtD2SWk9jQL6aTrUNP/Pfhq3be6+fJX0ryfpYry/6zGuTulP6k/NqAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c9cc63febb5b71713dfd184708656c6cb6628c9b45745921abe680eb80b4fa1","last_reissued_at":"2026-07-05T05:04:49.598232Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:04:49.598232Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Sample Efficiency Matters: A Benchmark for Practical Molecular Optimization","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.CE","authors_text":"Connor W. Coley, Jimeng Sun, Tianfan Fu, Wenhao Gao","submitted_at":"2022-06-22T20:36:49Z","abstract_excerpt":"Molecular optimization is a fundamental goal in the chemical sciences and is of central interest to drug and material design. In recent years, significant progress has been made in solving challenging problems across various aspects of computational molecular optimizations, emphasizing high validity, diversity, and, most recently, synthesizability. Despite this progress, many papers report results on trivial or self-designed tasks, bringing additional challenges to directly assessing the performance of new methods. Moreover, the sample efficiency of the optimization--the number of molecules ev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.12411","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/2206.12411/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":"2206.12411","created_at":"2026-07-05T05:04:49.598297+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.12411v2","created_at":"2026-07-05T05:04:49.598297+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.12411","created_at":"2026-07-05T05:04:49.598297+00:00"},{"alias_kind":"pith_short_12","alias_value":"LSOMMP7LWW3R","created_at":"2026-07-05T05:04:49.598297+00:00"},{"alias_kind":"pith_short_16","alias_value":"LSOMMP7LWW3ROE67","created_at":"2026-07-05T05:04:49.598297+00:00"},{"alias_kind":"pith_short_8","alias_value":"LSOMMP7L","created_at":"2026-07-05T05:04:49.598297+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.01328","citing_title":"Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3","json":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3.json","graph_json":"https://pith.science/api/pith-number/LSOMMP7LWW3ROE672GCHBBSWY3/graph.json","events_json":"https://pith.science/api/pith-number/LSOMMP7LWW3ROE672GCHBBSWY3/events.json","paper":"https://pith.science/paper/LSOMMP7L"},"agent_actions":{"view_html":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3","download_json":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3.json","view_paper":"https://pith.science/paper/LSOMMP7L","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.12411&json=true","fetch_graph":"https://pith.science/api/pith-number/LSOMMP7LWW3ROE672GCHBBSWY3/graph.json","fetch_events":"https://pith.science/api/pith-number/LSOMMP7LWW3ROE672GCHBBSWY3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3/action/storage_attestation","attest_author":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3/action/author_attestation","sign_citation":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3/action/citation_signature","submit_replication":"https://pith.science/pith/LSOMMP7LWW3ROE672GCHBBSWY3/action/replication_record"}},"created_at":"2026-07-05T05:04:49.598297+00:00","updated_at":"2026-07-05T05:04:49.598297+00:00"}