{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:WNEOHNFOP3VUXZOXHBBIHMWQKM","short_pith_number":"pith:WNEOHNFO","canonical_record":{"source":{"id":"2607.24314","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-27T11:58:56Z","cross_cats_sorted":[],"title_canon_sha256":"e289a1782f578788a02a73f556852af90e4999b395a2c3e7e6fb3554f78e4ca0","abstract_canon_sha256":"551bdc760c0dcac0515ff8ede489ada64298f71f5e9391683dc6f3a6c661fcbe"},"schema_version":"1.0"},"canonical_sha256":"b348e3b4ae7eeb4be5d7384283b2d05339fc68ba1afd46adc66a1003bd86ec61","source":{"kind":"arxiv","id":"2607.24314","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24314","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24314v1","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24314","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_12","alias_value":"WNEOHNFOP3VU","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_16","alias_value":"WNEOHNFOP3VUXZOX","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_8","alias_value":"WNEOHNFO","created_at":"2026-07-28T02:23:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:WNEOHNFOP3VUXZOXHBBIHMWQKM","target":"record","payload":{"canonical_record":{"source":{"id":"2607.24314","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-27T11:58:56Z","cross_cats_sorted":[],"title_canon_sha256":"e289a1782f578788a02a73f556852af90e4999b395a2c3e7e6fb3554f78e4ca0","abstract_canon_sha256":"551bdc760c0dcac0515ff8ede489ada64298f71f5e9391683dc6f3a6c661fcbe"},"schema_version":"1.0"},"canonical_sha256":"b348e3b4ae7eeb4be5d7384283b2d05339fc68ba1afd46adc66a1003bd86ec61","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T02:23:56.918605Z","signature_b64":"rYJy8tTau75JbsTA8Xsul9okxuNAI6CGF35WSKW4YNoAjsJZsGsEuyOTsutJAgqwSXEyioP8/QO5KVxenbMaBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b348e3b4ae7eeb4be5d7384283b2d05339fc68ba1afd46adc66a1003bd86ec61","last_reissued_at":"2026-07-28T02:23:56.917770Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T02:23:56.917770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.24314","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":"2026-07-28T02:23:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cTsO7gJ4HWcUvovAok0yrlm2hqxV9D0Is2Wq8jBKlWJfmqvyP5sloUtO96vbSZ++kQEj0UWZmBM//gkt70PuBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:09:48.794047Z"},"content_sha256":"115a5aac772e05a8a7251ecad7fe2c89ec3472c0eccdb43bb4ce049a57c39d33","schema_version":"1.0","event_id":"sha256:115a5aac772e05a8a7251ecad7fe2c89ec3472c0eccdb43bb4ce049a57c39d33"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:WNEOHNFOP3VUXZOXHBBIHMWQKM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Di Zhao, Guanghui Ren, Jinfeng Liu, Jingzhi Xue, Kedu Jin, Li-bin Wei, Shiyu Zhou, Tinghui Jin, Xiaoli Dai, XiJing Chen, Ying Li","submitted_at":"2026-07-27T11:58:56Z","abstract_excerpt":"Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24314","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/2607.24314/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":"2026-07-28T02:23:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jrJVw3np3qeglXEF8lcaID3Rr7KWOnopvkrLMgKa0naYNcXC61qs73B4G2XjA5aI8tGO53yH6c5sXq+4EdBYAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:09:48.795445Z"},"content_sha256":"dc75a7fba0c0bc5272d5c622b6c46cbfc4b8c4ae33898f351cd23ddb47bd22ed","schema_version":"1.0","event_id":"sha256:dc75a7fba0c0bc5272d5c622b6c46cbfc4b8c4ae33898f351cd23ddb47bd22ed"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:WNEOHNFOP3VUXZOXHBBIHMWQKM","target":"integrity","payload":{"note":"DOI is split by whitespace or line breaks in the printed bibliography. Reconstructed DOI 10.1145/2939672.2939785 resolves to 'XGBoost'. A reader following the printed text alone cannot reach it.","snippet":"Chen, T. & Guestrin, C. XGBoost: A Scalable Tree Boosting System. in Proceedings of the 22 nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (Association for Computing Machinery, New York, NY, USA, 2016).","arxiv_id":"2607.24314","detector":"doi_compliance","evidence":{"ref_index":44,"verdict_class":"incontrovertible","resolved_title":"XGBoost","printed_excerpt":"10.1145/2939672.293","reconstructed_doi":"10.1145/2939672.2939785"},"severity":"advisory","ref_index":44,"audited_at":"2026-07-31T18:41:41.889437Z","event_type":"pith.integrity.v1","detected_doi":"10.1145/2939672.2939785","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"894fe59c1b6edf80d0390ce72eecea2441701936f5c4254b437c6648b57b731c","paper_version":1,"verdict_class":"incontrovertible","resolved_title":"XGBoost","detector_version":"1.1.0","detected_arxiv_id":null,"integrity_event_id":14487,"payload_sha256":"35f7e1acae9598843ee3b11bc3a406e865a4e02f3bfe2b42b1010eddfeb564a5","signature_b64":"WGnx7074wK91igJVFaIvMF8+MBx6vLcLcAZgjcjUVCUIB2PmWjYW2QKcZJhzPWWW1V7adLXpUcUkPfgQrw2XAA==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-31T18:46:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"S8qly5g3zTgPzW/aTybgJf12chEK9W/BNly7aGEhNODWvOafsd7dcVNfmSk9Yo8bdG8iH+dJm2svjfZR2XzKDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T09:09:48.800985Z"},"content_sha256":"95a58fab6e1eefc723d19cb15fe79d4ea863d139b5bc021a3412404a4f3ad8f7","schema_version":"1.0","event_id":"sha256:95a58fab6e1eefc723d19cb15fe79d4ea863d139b5bc021a3412404a4f3ad8f7"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/bundle.json","state_url":"https://pith.science/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/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":"2026-08-04T09:09:48Z","links":{"resolver":"https://pith.science/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM","bundle":"https://pith.science/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/bundle.json","state":"https://pith.science/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WNEOHNFOP3VUXZOXHBBIHMWQKM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:WNEOHNFOP3VUXZOXHBBIHMWQKM","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"551bdc760c0dcac0515ff8ede489ada64298f71f5e9391683dc6f3a6c661fcbe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-27T11:58:56Z","title_canon_sha256":"e289a1782f578788a02a73f556852af90e4999b395a2c3e7e6fb3554f78e4ca0"},"schema_version":"1.0","source":{"id":"2607.24314","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.24314","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"arxiv_version","alias_value":"2607.24314v1","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.24314","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_12","alias_value":"WNEOHNFOP3VU","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_16","alias_value":"WNEOHNFOP3VUXZOX","created_at":"2026-07-28T02:23:56Z"},{"alias_kind":"pith_short_8","alias_value":"WNEOHNFO","created_at":"2026-07-28T02:23:56Z"}],"graph_snapshots":[{"event_id":"sha256:dc75a7fba0c0bc5272d5c622b6c46cbfc4b8c4ae33898f351cd23ddb47bd22ed","target":"graph","created_at":"2026-07-28T02:23:56Z","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/2607.24314/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predicting the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties of small molecules remains a major challenge in drug discovery. Here, we present MEGA-CL, a foundation graph neural network framework for universal molecular ADMET prediction. MEGA-CL integrates self-supervised contrastive learning with a multi-head external attention mechanism and an enhanced message-passing architecture, enabling simultaneous modeling of local chemical substructures and global inter-graph relationships while mitigating over-smoothing effects commonly observed in deep graph networks","authors_text":"Di Zhao, Guanghui Ren, Jinfeng Liu, Jingzhi Xue, Kedu Jin, Li-bin Wei, Shiyu Zhou, Tinghui Jin, Xiaoli Dai, XiJing Chen, Ying Li","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-27T11:58:56Z","title":"MEGA-CL: A Molecular Foundation Model for Generalizable ADMET Prediction through Graph External Attention and Contrastive Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.24314","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":"sha256:115a5aac772e05a8a7251ecad7fe2c89ec3472c0eccdb43bb4ce049a57c39d33","target":"record","created_at":"2026-07-28T02:23:56Z","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":"551bdc760c0dcac0515ff8ede489ada64298f71f5e9391683dc6f3a6c661fcbe","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-27T11:58:56Z","title_canon_sha256":"e289a1782f578788a02a73f556852af90e4999b395a2c3e7e6fb3554f78e4ca0"},"schema_version":"1.0","source":{"id":"2607.24314","kind":"arxiv","version":1}},"canonical_sha256":"b348e3b4ae7eeb4be5d7384283b2d05339fc68ba1afd46adc66a1003bd86ec61","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b348e3b4ae7eeb4be5d7384283b2d05339fc68ba1afd46adc66a1003bd86ec61","first_computed_at":"2026-07-28T02:23:56.917770Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-28T02:23:56.917770Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rYJy8tTau75JbsTA8Xsul9okxuNAI6CGF35WSKW4YNoAjsJZsGsEuyOTsutJAgqwSXEyioP8/QO5KVxenbMaBg==","signature_status":"signed_v1","signed_at":"2026-07-28T02:23:56.918605Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.24314","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:115a5aac772e05a8a7251ecad7fe2c89ec3472c0eccdb43bb4ce049a57c39d33","sha256:dc75a7fba0c0bc5272d5c622b6c46cbfc4b8c4ae33898f351cd23ddb47bd22ed","sha256:95a58fab6e1eefc723d19cb15fe79d4ea863d139b5bc021a3412404a4f3ad8f7"],"state_sha256":"bc1d42c7acf45103756149d9f34349c75c77bf40e685c31321ff218fc59fe101"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nJMr384ro5zRhHjVXOWeetQL62/mPmoL6+quZHZe2YXuhRdyBtc+9RRcUG44zaUsXD9IX5J+gYJ6sik8Kr1ZDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T09:09:48.804192Z","bundle_sha256":"fe9549fc9fb6eff32641be7afbc886322af0b4d7138fe652455346dc6ccf2606"}}