{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:QGDAJYM6YRSAGZGDNFRCMDALK6","short_pith_number":"pith:QGDAJYM6","canonical_record":{"source":{"id":"2203.08031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-15T16:14:30Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"c4f71fe4612046095881a187d0d59cb0594f503e89f79ba6c5bc0c534a0063d4","abstract_canon_sha256":"0e460698488ba840b886c89acf719331b54b17058005b72ba27c27a74196dc79"},"schema_version":"1.0"},"canonical_sha256":"818604e19ec4640364c36962260c0b578089c4e02998793ff18acdbb86b82f8c","source":{"kind":"arxiv","id":"2203.08031","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.08031","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"arxiv_version","alias_value":"2203.08031v1","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.08031","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_12","alias_value":"QGDAJYM6YRSA","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_16","alias_value":"QGDAJYM6YRSAGZGD","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_8","alias_value":"QGDAJYM6","created_at":"2026-07-05T04:05:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:QGDAJYM6YRSAGZGDNFRCMDALK6","target":"record","payload":{"canonical_record":{"source":{"id":"2203.08031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-15T16:14:30Z","cross_cats_sorted":["q-bio.BM"],"title_canon_sha256":"c4f71fe4612046095881a187d0d59cb0594f503e89f79ba6c5bc0c534a0063d4","abstract_canon_sha256":"0e460698488ba840b886c89acf719331b54b17058005b72ba27c27a74196dc79"},"schema_version":"1.0"},"canonical_sha256":"818604e19ec4640364c36962260c0b578089c4e02998793ff18acdbb86b82f8c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:05:25.332427Z","signature_b64":"ABkVvV34Z3Mk+jB+5gimlfEZUwPy4j8bwTnQTIgMWdd+MqGPVSQsFfchsBM6M+DI9Yt9EOoWMdOaDJ7TdyFLBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"818604e19ec4640364c36962260c0b578089c4e02998793ff18acdbb86b82f8c","last_reissued_at":"2026-07-05T04:05:25.332016Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:05:25.332016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.08031","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":"2026-07-05T04:05:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I/4zA5RfkAtTfaBi+izmkfMxkGkOrg1I3fRJTkMOVSqil7Z8TxAKbIoLPXCb8bS6KqXqH9JgUXH5hu+GRwvNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T04:26:31.077715Z"},"content_sha256":"c501ed7513f07bc99d64579e800db0d352df3788d60ece7cb256fd3a64ccedc5","schema_version":"1.0","event_id":"sha256:c501ed7513f07bc99d64579e800db0d352df3788d60ece7cb256fd3a64ccedc5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:QGDAJYM6YRSAGZGDNFRCMDALK6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Data-Efficient Graph Grammar Learning for Molecular Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Beichen Li, Jie Chen, Minghao Guo, Payel Das, Veronika Thost, Wojciech Matusik","submitted_at":"2022-03-15T16:14:30Z","abstract_excerpt":"The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. This presents a considerable challenge for the deep learning generative models to comprehensively describe the molecular design space. Another major challenge is to generate only physically synthesizable molecule"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.08031","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/2203.08031/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":"2026-07-05T04:05:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/N0WdFpfIHG5vOMzi9CKhL+aT6ZphVXDlhqEH8JSdCbzt08vPTk1FeC06Smw67A8Zct26AdX4Gy06pSR6ISECg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-06T04:26:31.078653Z"},"content_sha256":"b9e858524e407c096900eb0af844d158525f58fcae2d4d82fbad0f9a3534eb81","schema_version":"1.0","event_id":"sha256:b9e858524e407c096900eb0af844d158525f58fcae2d4d82fbad0f9a3534eb81"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/bundle.json","state_url":"https://pith.science/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/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":"2026-08-06T04:26:31Z","links":{"resolver":"https://pith.science/pith/QGDAJYM6YRSAGZGDNFRCMDALK6","bundle":"https://pith.science/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/bundle.json","state":"https://pith.science/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QGDAJYM6YRSAGZGDNFRCMDALK6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:QGDAJYM6YRSAGZGDNFRCMDALK6","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":"0e460698488ba840b886c89acf719331b54b17058005b72ba27c27a74196dc79","cross_cats_sorted":["q-bio.BM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-15T16:14:30Z","title_canon_sha256":"c4f71fe4612046095881a187d0d59cb0594f503e89f79ba6c5bc0c534a0063d4"},"schema_version":"1.0","source":{"id":"2203.08031","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.08031","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"arxiv_version","alias_value":"2203.08031v1","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.08031","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_12","alias_value":"QGDAJYM6YRSA","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_16","alias_value":"QGDAJYM6YRSAGZGD","created_at":"2026-07-05T04:05:25Z"},{"alias_kind":"pith_short_8","alias_value":"QGDAJYM6","created_at":"2026-07-05T04:05:25Z"}],"graph_snapshots":[{"event_id":"sha256:b9e858524e407c096900eb0af844d158525f58fcae2d4d82fbad0f9a3534eb81","target":"graph","created_at":"2026-07-05T04:05:25Z","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/2203.08031/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The problem of molecular generation has received significant attention recently. Existing methods are typically based on deep neural networks and require training on large datasets with tens of thousands of samples. In practice, however, the size of class-specific chemical datasets is usually limited (e.g., dozens of samples) due to labor-intensive experimentation and data collection. This presents a considerable challenge for the deep learning generative models to comprehensively describe the molecular design space. Another major challenge is to generate only physically synthesizable molecule","authors_text":"Beichen Li, Jie Chen, Minghao Guo, Payel Das, Veronika Thost, Wojciech Matusik","cross_cats":["q-bio.BM"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-15T16:14:30Z","title":"Data-Efficient Graph Grammar Learning for Molecular Generation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.08031","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:c501ed7513f07bc99d64579e800db0d352df3788d60ece7cb256fd3a64ccedc5","target":"record","created_at":"2026-07-05T04:05:25Z","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":"0e460698488ba840b886c89acf719331b54b17058005b72ba27c27a74196dc79","cross_cats_sorted":["q-bio.BM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-03-15T16:14:30Z","title_canon_sha256":"c4f71fe4612046095881a187d0d59cb0594f503e89f79ba6c5bc0c534a0063d4"},"schema_version":"1.0","source":{"id":"2203.08031","kind":"arxiv","version":1}},"canonical_sha256":"818604e19ec4640364c36962260c0b578089c4e02998793ff18acdbb86b82f8c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"818604e19ec4640364c36962260c0b578089c4e02998793ff18acdbb86b82f8c","first_computed_at":"2026-07-05T04:05:25.332016Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:05:25.332016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ABkVvV34Z3Mk+jB+5gimlfEZUwPy4j8bwTnQTIgMWdd+MqGPVSQsFfchsBM6M+DI9Yt9EOoWMdOaDJ7TdyFLBA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:05:25.332427Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.08031","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c501ed7513f07bc99d64579e800db0d352df3788d60ece7cb256fd3a64ccedc5","sha256:b9e858524e407c096900eb0af844d158525f58fcae2d4d82fbad0f9a3534eb81"],"state_sha256":"c0f76a2a0723460b644ef8134152af78045cc635930c047fe66e38dc13fce4fd"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aNdkLn0nG6t0L7KEBhXWzUtAR3fIwDa/lhtj49ekoCenAJPas1oJCfMpK0wVugckIanLYHB8k/u9AP2Rtt6cCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-06T04:26:31.090563Z","bundle_sha256":"df1e9b9c5ae5bed2a539fbd4aa2c33426c8ae93550187f86fbe3895b9b25ddc3"}}