{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EZKFGPLVXMVXN6FFE2SFQNESXS","short_pith_number":"pith:EZKFGPLV","schema_version":"1.0","canonical_sha256":"2654533d75bb2b76f8a526a4583492bcaeca3d2c19de17afd70ab96d2f112b21","source":{"kind":"arxiv","id":"2410.05289","version":4},"attestation_state":"computed","paper":{"title":"MARS: A neurosymbolic approach for interpretable drug discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Daniel Domingo-Fern\\'andez, Jacques D. Fleuriot, Lauren Nicole DeLong, Paola Galdi, Yojana Gadiya","submitted_at":"2024-10-02T14:14:17Z","abstract_excerpt":"Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, no clear guidelines exist to assess the biological plausibility of model interpretations.\n  Methods: To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowled"},"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":"2410.05289","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2024-10-02T14:14:17Z","cross_cats_sorted":["cs.LG","cs.LO"],"title_canon_sha256":"8e4beb77a1f2844b9dd31557009a0fc716a9e3fb2c29dc1962167bb1d550d79b","abstract_canon_sha256":"e26fbb5048e5cc523c03a6bfe7f583f070c46295a8df7f24c819f1925c3dfbf5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T01:17:20.647544Z","signature_b64":"JQAJJLNwn0BQmvrIkxkp/XcweJPzAi10TPclGw6wcuExuGVeew2DDhOA8eym6mgg/xdOPl9LDrEbjUy75O3NDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2654533d75bb2b76f8a526a4583492bcaeca3d2c19de17afd70ab96d2f112b21","last_reissued_at":"2026-06-30T01:17:20.646699Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T01:17:20.646699Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MARS: A neurosymbolic approach for interpretable drug discovery","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","cs.LO"],"primary_cat":"cs.AI","authors_text":"Daniel Domingo-Fern\\'andez, Jacques D. Fleuriot, Lauren Nicole DeLong, Paola Galdi, Yojana Gadiya","submitted_at":"2024-10-02T14:14:17Z","abstract_excerpt":"Background: Neurosymbolic (NeSy) artificial intelligence describes the combination of logic or rule-based techniques with neural networks. Compared to neural approaches, NeSy methods often possess enhanced interpretability, which is particularly promising for biomedical applications like drug discovery. However, no clear guidelines exist to assess the biological plausibility of model interpretations.\n  Methods: To assess interpretability in the context of drug discovery, we devise a novel prediction task, called drug mechanism-of-action (MoA) deconvolution, with an associated, tailored knowled"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.05289","kind":"arxiv","version":4},"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/2410.05289/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":"2410.05289","created_at":"2026-06-30T01:17:20.646819+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.05289v4","created_at":"2026-06-30T01:17:20.646819+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.05289","created_at":"2026-06-30T01:17:20.646819+00:00"},{"alias_kind":"pith_short_12","alias_value":"EZKFGPLVXMVX","created_at":"2026-06-30T01:17:20.646819+00:00"},{"alias_kind":"pith_short_16","alias_value":"EZKFGPLVXMVXN6FF","created_at":"2026-06-30T01:17:20.646819+00:00"},{"alias_kind":"pith_short_8","alias_value":"EZKFGPLV","created_at":"2026-06-30T01:17:20.646819+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/EZKFGPLVXMVXN6FFE2SFQNESXS","json":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS.json","graph_json":"https://pith.science/api/pith-number/EZKFGPLVXMVXN6FFE2SFQNESXS/graph.json","events_json":"https://pith.science/api/pith-number/EZKFGPLVXMVXN6FFE2SFQNESXS/events.json","paper":"https://pith.science/paper/EZKFGPLV"},"agent_actions":{"view_html":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS","download_json":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS.json","view_paper":"https://pith.science/paper/EZKFGPLV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.05289&json=true","fetch_graph":"https://pith.science/api/pith-number/EZKFGPLVXMVXN6FFE2SFQNESXS/graph.json","fetch_events":"https://pith.science/api/pith-number/EZKFGPLVXMVXN6FFE2SFQNESXS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS/action/storage_attestation","attest_author":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS/action/author_attestation","sign_citation":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS/action/citation_signature","submit_replication":"https://pith.science/pith/EZKFGPLVXMVXN6FFE2SFQNESXS/action/replication_record"}},"created_at":"2026-06-30T01:17:20.646819+00:00","updated_at":"2026-06-30T01:17:20.646819+00:00"}