{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:CLGQS6I3UL6D4HDH5MI3AW76FL","short_pith_number":"pith:CLGQS6I3","canonical_record":{"source":{"id":"2411.13688","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T20:07:48Z","cross_cats_sorted":["q-bio.BM","stat.ML"],"title_canon_sha256":"a807b40dae0986254e27f0067694b96b676083f1fa4f06193a5be2d5fb64645c","abstract_canon_sha256":"4f8e70c99dc2dbb13769ab54bd73fb3467a2017b36d7aa3852c0da6d04336546"},"schema_version":"1.0"},"canonical_sha256":"12cd09791ba2fc3e1c67eb11b05bfe2ae4c75fa8868ae4a6f5f6053b95d13293","source":{"kind":"arxiv","id":"2411.13688","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.13688","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"2411.13688v1","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13688","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"CLGQS6I3UL6D","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_16","alias_value":"CLGQS6I3UL6D4HDH","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_8","alias_value":"CLGQS6I3","created_at":"2026-07-05T09:38:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:CLGQS6I3UL6D4HDH5MI3AW76FL","target":"record","payload":{"canonical_record":{"source":{"id":"2411.13688","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T20:07:48Z","cross_cats_sorted":["q-bio.BM","stat.ML"],"title_canon_sha256":"a807b40dae0986254e27f0067694b96b676083f1fa4f06193a5be2d5fb64645c","abstract_canon_sha256":"4f8e70c99dc2dbb13769ab54bd73fb3467a2017b36d7aa3852c0da6d04336546"},"schema_version":"1.0"},"canonical_sha256":"12cd09791ba2fc3e1c67eb11b05bfe2ae4c75fa8868ae4a6f5f6053b95d13293","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:38:35.549405Z","signature_b64":"v70AYArdh4Qr4cIO+QaJOhcmuannPhQEOZ7tX8fCouNL2Y2t2/+x5frQGhV+GilNY4VFdVspQoe5JtEzFapNAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"12cd09791ba2fc3e1c67eb11b05bfe2ae4c75fa8868ae4a6f5f6053b95d13293","last_reissued_at":"2026-07-05T09:38:35.548890Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:38:35.548890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.13688","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SPhtJFv2yfv8R1fMviStaEQxJ0M47Ogt7WTAal7azcu1gEqAZPm5ff9v5flYzpgBiJl2m9UbG8dJLzc0gDtTBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:45:17.816598Z"},"content_sha256":"0a14d1f364c6971660d53fd88747e2fb5f964742901dee077f08d10de4d96bea","schema_version":"1.0","event_id":"sha256:0a14d1f364c6971660d53fd88747e2fb5f964742901dee077f08d10de4d96bea"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:CLGQS6I3UL6D4HDH5MI3AW76FL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM","stat.ML"],"primary_cat":"cs.LG","authors_text":"Markus Dablander","submitted_at":"2024-11-20T20:07:48Z","abstract_excerpt":"Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computational drug discovery. Recently, message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs. While such techniques hold great promise, further investigations are needed to clarify if and when they indeed manage to definitively outcompete classical molecular featurisations such as extended-connectivity fingerprints (ECFPs) and physicochem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13688","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/2411.13688/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":"2026-07-05T09:38:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uLOjOvfF9lro2XWMU5RryQ6QFInebWdBhHIfskON3NIlyu2X+pBIz3d1uJQntpQNk+P3HVN+cpK9GCe5jFOECA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T04:45:17.816976Z"},"content_sha256":"585ab2a305ad82ff7a4127f431e17f97d7bf88eed4c43f65d1bb9abdf6c59f21","schema_version":"1.0","event_id":"sha256:585ab2a305ad82ff7a4127f431e17f97d7bf88eed4c43f65d1bb9abdf6c59f21"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/bundle.json","state_url":"https://pith.science/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/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":"2026-08-13T04:45:17Z","links":{"resolver":"https://pith.science/pith/CLGQS6I3UL6D4HDH5MI3AW76FL","bundle":"https://pith.science/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/bundle.json","state":"https://pith.science/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CLGQS6I3UL6D4HDH5MI3AW76FL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:CLGQS6I3UL6D4HDH5MI3AW76FL","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":"4f8e70c99dc2dbb13769ab54bd73fb3467a2017b36d7aa3852c0da6d04336546","cross_cats_sorted":["q-bio.BM","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T20:07:48Z","title_canon_sha256":"a807b40dae0986254e27f0067694b96b676083f1fa4f06193a5be2d5fb64645c"},"schema_version":"1.0","source":{"id":"2411.13688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.13688","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"arxiv_version","alias_value":"2411.13688v1","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.13688","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_12","alias_value":"CLGQS6I3UL6D","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_16","alias_value":"CLGQS6I3UL6D4HDH","created_at":"2026-07-05T09:38:35Z"},{"alias_kind":"pith_short_8","alias_value":"CLGQS6I3","created_at":"2026-07-05T09:38:35Z"}],"graph_snapshots":[{"event_id":"sha256:585ab2a305ad82ff7a4127f431e17f97d7bf88eed4c43f65d1bb9abdf6c59f21","target":"graph","created_at":"2026-07-05T09:38:35Z","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/2411.13688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Molecular featurisation refers to the transformation of molecular data into numerical feature vectors. It is one of the key research areas in molecular machine learning and computational drug discovery. Recently, message-passing graph neural networks (GNNs) have emerged as a novel method to learn differentiable features directly from molecular graphs. While such techniques hold great promise, further investigations are needed to clarify if and when they indeed manage to definitively outcompete classical molecular featurisations such as extended-connectivity fingerprints (ECFPs) and physicochem","authors_text":"Markus Dablander","cross_cats":["q-bio.BM","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T20:07:48Z","title":"Investigating Graph Neural Networks and Classical Feature-Extraction Techniques in Activity-Cliff and Molecular Property Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.13688","kind":"arxiv","version":1},"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":"sha256:0a14d1f364c6971660d53fd88747e2fb5f964742901dee077f08d10de4d96bea","target":"record","created_at":"2026-07-05T09:38:35Z","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":"4f8e70c99dc2dbb13769ab54bd73fb3467a2017b36d7aa3852c0da6d04336546","cross_cats_sorted":["q-bio.BM","stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-11-20T20:07:48Z","title_canon_sha256":"a807b40dae0986254e27f0067694b96b676083f1fa4f06193a5be2d5fb64645c"},"schema_version":"1.0","source":{"id":"2411.13688","kind":"arxiv","version":1}},"canonical_sha256":"12cd09791ba2fc3e1c67eb11b05bfe2ae4c75fa8868ae4a6f5f6053b95d13293","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"12cd09791ba2fc3e1c67eb11b05bfe2ae4c75fa8868ae4a6f5f6053b95d13293","first_computed_at":"2026-07-05T09:38:35.548890Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:38:35.548890Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"v70AYArdh4Qr4cIO+QaJOhcmuannPhQEOZ7tX8fCouNL2Y2t2/+x5frQGhV+GilNY4VFdVspQoe5JtEzFapNAw==","signature_status":"signed_v1","signed_at":"2026-07-05T09:38:35.549405Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.13688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a14d1f364c6971660d53fd88747e2fb5f964742901dee077f08d10de4d96bea","sha256:585ab2a305ad82ff7a4127f431e17f97d7bf88eed4c43f65d1bb9abdf6c59f21"],"state_sha256":"20bb62811bfc7caed4f489c6e016215b73ef65a48b1048224699f221de4784c7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sDehftLlLNQWOw664z6LF4oG/j6hGY4o/gAwNy+STzBj+Lk0oik7aRjowVnOuGBkE2+kqTcjNF62zPAuLQgcAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T04:45:17.819721Z","bundle_sha256":"443f07a2cd545ffc119c9e0d67fbd89e3129d4d145f4ea308e71a7790ea7776d"}}