{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:WMXSFMJ4452NTKLJEXR2WF7G5U","short_pith_number":"pith:WMXSFMJ4","schema_version":"1.0","canonical_sha256":"b32f22b13ce774d9a96925e3ab17e6ed185dea99a994fb28a7c0c6f64ebdfbee","source":{"kind":"arxiv","id":"2502.06891","version":3},"attestation_state":"computed","paper":{"title":"ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Ian Foster, Jinbo Xu, Rick Stevens, Songhao Jiang, Xuefeng Liu","submitted_at":"2025-02-09T10:36:33Z","abstract_excerpt":"Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates retaining the beneficial properties of the original drug while simultaneously enhancing desired attributes beyond its scope. In this work, we aim to tackle this challenge by introducing ScaffoldGPT, a novel Generative Pretrained Transformer (GPT) designed for drug optimization based on molecular scaffolds. Our work comprises three key components: (1) A three-stage drug optimization approach that integrates pretraining"},"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":"2502.06891","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.BM","submitted_at":"2025-02-09T10:36:33Z","cross_cats_sorted":["cs.CL","cs.LG"],"title_canon_sha256":"386574a253d0c2106c7988da56cba1484ab8346b21e13d8ae16377d2dc97db65","abstract_canon_sha256":"b7d87be87140b06098382722c2e647157eead2b8db7e6d08a22ce5914ca022c6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:51:21.883569Z","signature_b64":"yQ8P+v90hGmUUp6V0srSsiqwvL7rCNcj3UHZa2ENcsoS7m3lkaubgA/UeuqVqkSEFyK9Z8Hix15IcMjWBJPYCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b32f22b13ce774d9a96925e3ab17e6ed185dea99a994fb28a7c0c6f64ebdfbee","last_reissued_at":"2026-07-05T11:51:21.883021Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:51:21.883021Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"ScaffoldGPT: A Scaffold-based GPT Model for Drug Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL","cs.LG"],"primary_cat":"q-bio.BM","authors_text":"Ian Foster, Jinbo Xu, Rick Stevens, Songhao Jiang, Xuefeng Liu","submitted_at":"2025-02-09T10:36:33Z","abstract_excerpt":"Drug optimization has become increasingly crucial in light of fast-mutating virus strains and drug-resistant cancer cells. Nevertheless, it remains challenging as it necessitates retaining the beneficial properties of the original drug while simultaneously enhancing desired attributes beyond its scope. In this work, we aim to tackle this challenge by introducing ScaffoldGPT, a novel Generative Pretrained Transformer (GPT) designed for drug optimization based on molecular scaffolds. Our work comprises three key components: (1) A three-stage drug optimization approach that integrates pretraining"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06891","kind":"arxiv","version":3},"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/2502.06891/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":"2502.06891","created_at":"2026-07-05T11:51:21.883093+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.06891v3","created_at":"2026-07-05T11:51:21.883093+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.06891","created_at":"2026-07-05T11:51:21.883093+00:00"},{"alias_kind":"pith_short_12","alias_value":"WMXSFMJ4452N","created_at":"2026-07-05T11:51:21.883093+00:00"},{"alias_kind":"pith_short_16","alias_value":"WMXSFMJ4452NTKLJ","created_at":"2026-07-05T11:51:21.883093+00:00"},{"alias_kind":"pith_short_8","alias_value":"WMXSFMJ4","created_at":"2026-07-05T11:51:21.883093+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/WMXSFMJ4452NTKLJEXR2WF7G5U","json":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U.json","graph_json":"https://pith.science/api/pith-number/WMXSFMJ4452NTKLJEXR2WF7G5U/graph.json","events_json":"https://pith.science/api/pith-number/WMXSFMJ4452NTKLJEXR2WF7G5U/events.json","paper":"https://pith.science/paper/WMXSFMJ4"},"agent_actions":{"view_html":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U","download_json":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U.json","view_paper":"https://pith.science/paper/WMXSFMJ4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.06891&json=true","fetch_graph":"https://pith.science/api/pith-number/WMXSFMJ4452NTKLJEXR2WF7G5U/graph.json","fetch_events":"https://pith.science/api/pith-number/WMXSFMJ4452NTKLJEXR2WF7G5U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U/action/storage_attestation","attest_author":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U/action/author_attestation","sign_citation":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U/action/citation_signature","submit_replication":"https://pith.science/pith/WMXSFMJ4452NTKLJEXR2WF7G5U/action/replication_record"}},"created_at":"2026-07-05T11:51:21.883093+00:00","updated_at":"2026-07-05T11:51:21.883093+00:00"}