{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:3JXT2YPU2SDGAQRS4K7MMCRO6E","short_pith_number":"pith:3JXT2YPU","canonical_record":{"source":{"id":"2306.11768","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-06-20T14:21:58Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"d5db1c4fc24377e5136f4e546e4956f86391ed9966399476a972a1434f70216c","abstract_canon_sha256":"530ad77bfe2ca55f805f87507235f948b92387544749a893a3fa4489689e8cb7"},"schema_version":"1.0"},"canonical_sha256":"da6f3d61f4d486604232e2bec60a2ef135362ca445cc5ad9c5ccd6a1c571a0c3","source":{"kind":"arxiv","id":"2306.11768","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11768","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11768v6","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11768","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_12","alias_value":"3JXT2YPU2SDG","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_16","alias_value":"3JXT2YPU2SDGAQRS","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_8","alias_value":"3JXT2YPU","created_at":"2026-07-05T09:36:07Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:3JXT2YPU2SDGAQRS4K7MMCRO6E","target":"record","payload":{"canonical_record":{"source":{"id":"2306.11768","kind":"arxiv","version":6},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-06-20T14:21:58Z","cross_cats_sorted":["cs.CE","cs.LG"],"title_canon_sha256":"d5db1c4fc24377e5136f4e546e4956f86391ed9966399476a972a1434f70216c","abstract_canon_sha256":"530ad77bfe2ca55f805f87507235f948b92387544749a893a3fa4489689e8cb7"},"schema_version":"1.0"},"canonical_sha256":"da6f3d61f4d486604232e2bec60a2ef135362ca445cc5ad9c5ccd6a1c571a0c3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:36:07.380074Z","signature_b64":"uBwKpu9UmjRYFlomFJvVV8R2qk0yQ4wwQPKErC6SvKvFzYrct1CB/W0ytS49tpecfHZA5Z4hoxbHvjiHL2vaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da6f3d61f4d486604232e2bec60a2ef135362ca445cc5ad9c5ccd6a1c571a0c3","last_reissued_at":"2026-07-05T09:36:07.379543Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:36:07.379543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2306.11768","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LUJvlGPol24Oovv9hOWQu5y65c1kCjyq3ouFMbvgynjDXCnKOX44EXchYrOdFKD9LscbjCPDne7EHEg8nCkTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:07:16.753161Z"},"content_sha256":"d9b7b8e9bcae0f114cb268abbfe7d64475fd3cf0982735f840568e9c616abb20","schema_version":"1.0","event_id":"sha256:d9b7b8e9bcae0f114cb268abbfe7d64475fd3cf0982735f840568e9c616abb20"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:3JXT2YPU2SDGAQRS4K7MMCRO6E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Geometric Deep Learning for Structure-Based Drug Design: A Survey","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CE","cs.LG"],"primary_cat":"q-bio.QM","authors_text":"Enhong Chen, Jiaxian Yan, Marinka Zitnik, Mengdi Wang, Qi Liu, Yining Huang, Zaixi Zhang","submitted_at":"2023-06-20T14:21:58Z","abstract_excerpt":"Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical modeling and domain expertise, are often resource-intensive. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, alongside breakthroughs in accurate protein structure predictions from tools like AlphaFold, have significantly propelled the field forward. This paper systematically reviews the state-of-the-art in geometric deep learning for SBDD. We begin by outlining "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11768","kind":"arxiv","version":6},"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/2306.11768/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:36:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"paAI2L/alCCaBY/Y+NP8UHy5kNd2ct5F1WFPcc19uFv4trcYE6jVEEaMBDrv0tFQOIZI8npLgQt4uZWdQrK5Bg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T22:07:16.754089Z"},"content_sha256":"e8b6f83feb45c8ae68fe48e16d778050bff0f39a8bc96d38ad3f27f176bd39d4","schema_version":"1.0","event_id":"sha256:e8b6f83feb45c8ae68fe48e16d778050bff0f39a8bc96d38ad3f27f176bd39d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/bundle.json","state_url":"https://pith.science/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T22:07:16Z","links":{"resolver":"https://pith.science/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E","bundle":"https://pith.science/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/bundle.json","state":"https://pith.science/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3JXT2YPU2SDGAQRS4K7MMCRO6E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:3JXT2YPU2SDGAQRS4K7MMCRO6E","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"530ad77bfe2ca55f805f87507235f948b92387544749a893a3fa4489689e8cb7","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-06-20T14:21:58Z","title_canon_sha256":"d5db1c4fc24377e5136f4e546e4956f86391ed9966399476a972a1434f70216c"},"schema_version":"1.0","source":{"id":"2306.11768","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.11768","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"arxiv_version","alias_value":"2306.11768v6","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11768","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_12","alias_value":"3JXT2YPU2SDG","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_16","alias_value":"3JXT2YPU2SDGAQRS","created_at":"2026-07-05T09:36:07Z"},{"alias_kind":"pith_short_8","alias_value":"3JXT2YPU","created_at":"2026-07-05T09:36:07Z"}],"graph_snapshots":[{"event_id":"sha256:e8b6f83feb45c8ae68fe48e16d778050bff0f39a8bc96d38ad3f27f176bd39d4","target":"graph","created_at":"2026-07-05T09:36:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.11768/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Traditional approaches, rooted in physicochemical modeling and domain expertise, are often resource-intensive. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, alongside breakthroughs in accurate protein structure predictions from tools like AlphaFold, have significantly propelled the field forward. This paper systematically reviews the state-of-the-art in geometric deep learning for SBDD. We begin by outlining ","authors_text":"Enhong Chen, Jiaxian Yan, Marinka Zitnik, Mengdi Wang, Qi Liu, Yining Huang, Zaixi Zhang","cross_cats":["cs.CE","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-06-20T14:21:58Z","title":"Geometric Deep Learning for Structure-Based Drug Design: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11768","kind":"arxiv","version":6},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:d9b7b8e9bcae0f114cb268abbfe7d64475fd3cf0982735f840568e9c616abb20","target":"record","created_at":"2026-07-05T09:36:07Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"530ad77bfe2ca55f805f87507235f948b92387544749a893a3fa4489689e8cb7","cross_cats_sorted":["cs.CE","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"q-bio.QM","submitted_at":"2023-06-20T14:21:58Z","title_canon_sha256":"d5db1c4fc24377e5136f4e546e4956f86391ed9966399476a972a1434f70216c"},"schema_version":"1.0","source":{"id":"2306.11768","kind":"arxiv","version":6}},"canonical_sha256":"da6f3d61f4d486604232e2bec60a2ef135362ca445cc5ad9c5ccd6a1c571a0c3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"da6f3d61f4d486604232e2bec60a2ef135362ca445cc5ad9c5ccd6a1c571a0c3","first_computed_at":"2026-07-05T09:36:07.379543Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:36:07.379543Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uBwKpu9UmjRYFlomFJvVV8R2qk0yQ4wwQPKErC6SvKvFzYrct1CB/W0ytS49tpecfHZA5Z4hoxbHvjiHL2vaBg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:36:07.380074Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.11768","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d9b7b8e9bcae0f114cb268abbfe7d64475fd3cf0982735f840568e9c616abb20","sha256:e8b6f83feb45c8ae68fe48e16d778050bff0f39a8bc96d38ad3f27f176bd39d4"],"state_sha256":"68ef3ab5427fd30bbfe7746a6d56ab37bd02cf47638846d258481fe1265b57bd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AXTnzOUj4vaoWIGOHsbUmSx78GtVv45I73ehjEOD/U4VZjufCF0gFYRQYfP7JyNUNd8wfrcX4qX8ba9U49VjDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T22:07:16.760318Z","bundle_sha256":"d355d5feeb0bc0eaa1d7ee578cebc1370e8fbdc1df495eeeb2f5dbcc03b42c06"}}