{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:KVVGHXUOP5XT3BGF4RVZL6MSB4","short_pith_number":"pith:KVVGHXUO","schema_version":"1.0","canonical_sha256":"556a63de8e7f6f3d84c5e46b95f9920f3f4949fcdbf4bf4ce3e5028b768fd296","source":{"kind":"arxiv","id":"2407.04424","version":1},"attestation_state":"computed","paper":{"title":"Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"q-bio.QM","authors_text":"Andreas Bender, Benoit Baillif, Jason Cole, Patrick McCabe","submitted_at":"2024-07-05T11:17:18Z","abstract_excerpt":"Three-dimensional (3D) deep molecular generative models offer the advantage of goal-directed generation based on 3D-dependent properties, such as binding affinity for structure-based design within binding pockets. Traditional benchmarks created to evaluate SMILES or molecular graphs generators, such as GuacaMol or MOSES, are limited to evaluate 3D generators as they do not assess the quality of the generated molecular conformation. In this work, we hence developed GenBench3D, which implements a new benchmark for models producing molecules within a binding pocket. Our main contribution is the V"},"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":"2407.04424","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.QM","submitted_at":"2024-07-05T11:17:18Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"9fde222c53c434e5446f565ddeeea58d52b06b444ddf4254be2390a81c46257d","abstract_canon_sha256":"de54ff8f7e2e74dd2c294f3908175a6ebd0a9c9a36733a2682a0736ff4f28d3b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:40:33.613099Z","signature_b64":"+j1GFyW87jv6zyXMRwKWWmTXiDq1mcOQ1AvZgOUKQlpBD7xSTLldNUIdLTXd2SPzOct244miPaTzw9N+pl9ECQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"556a63de8e7f6f3d84c5e46b95f9920f3f4949fcdbf4bf4ce3e5028b768fd296","last_reissued_at":"2026-07-05T08:40:33.612692Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:40:33.612692Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Benchmarking structure-based three-dimensional molecular generative models using GenBench3D: ligand conformation quality matters","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"q-bio.QM","authors_text":"Andreas Bender, Benoit Baillif, Jason Cole, Patrick McCabe","submitted_at":"2024-07-05T11:17:18Z","abstract_excerpt":"Three-dimensional (3D) deep molecular generative models offer the advantage of goal-directed generation based on 3D-dependent properties, such as binding affinity for structure-based design within binding pockets. Traditional benchmarks created to evaluate SMILES or molecular graphs generators, such as GuacaMol or MOSES, are limited to evaluate 3D generators as they do not assess the quality of the generated molecular conformation. In this work, we hence developed GenBench3D, which implements a new benchmark for models producing molecules within a binding pocket. Our main contribution is the V"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.04424","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/2407.04424/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":"2407.04424","created_at":"2026-07-05T08:40:33.612751+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.04424v1","created_at":"2026-07-05T08:40:33.612751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.04424","created_at":"2026-07-05T08:40:33.612751+00:00"},{"alias_kind":"pith_short_12","alias_value":"KVVGHXUOP5XT","created_at":"2026-07-05T08:40:33.612751+00:00"},{"alias_kind":"pith_short_16","alias_value":"KVVGHXUOP5XT3BGF","created_at":"2026-07-05T08:40:33.612751+00:00"},{"alias_kind":"pith_short_8","alias_value":"KVVGHXUO","created_at":"2026-07-05T08:40:33.612751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.08005","citing_title":"Unraveling the Potential of Diffusion Models in Small Molecule Generation","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4","json":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4.json","graph_json":"https://pith.science/api/pith-number/KVVGHXUOP5XT3BGF4RVZL6MSB4/graph.json","events_json":"https://pith.science/api/pith-number/KVVGHXUOP5XT3BGF4RVZL6MSB4/events.json","paper":"https://pith.science/paper/KVVGHXUO"},"agent_actions":{"view_html":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4","download_json":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4.json","view_paper":"https://pith.science/paper/KVVGHXUO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.04424&json=true","fetch_graph":"https://pith.science/api/pith-number/KVVGHXUOP5XT3BGF4RVZL6MSB4/graph.json","fetch_events":"https://pith.science/api/pith-number/KVVGHXUOP5XT3BGF4RVZL6MSB4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4/action/storage_attestation","attest_author":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4/action/author_attestation","sign_citation":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4/action/citation_signature","submit_replication":"https://pith.science/pith/KVVGHXUOP5XT3BGF4RVZL6MSB4/action/replication_record"}},"created_at":"2026-07-05T08:40:33.612751+00:00","updated_at":"2026-07-05T08:40:33.612751+00:00"}