{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:OEACZTGCQOSB4EHLHXHPNFYDF5","short_pith_number":"pith:OEACZTGC","schema_version":"1.0","canonical_sha256":"71002cccc283a41e10eb3dcef697032f71ca101e68acaa686ad1995cafe7b12b","source":{"kind":"arxiv","id":"2607.01362","version":1},"attestation_state":"computed","paper":{"title":"Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","q-bio.BM"],"primary_cat":"physics.chem-ph","authors_text":"Heather J. Kulik, Weiliang Luo","submitted_at":"2026-07-01T18:24:10Z","abstract_excerpt":"Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce this cost, but enzymes present challenges beyond small molecules, including large system sizes, implicit-solvent environments, substantial polarization, and charge transfer. Here, we present an integrated software framework for efficient NNP training for mechanistic studies of enzymes, demonstrated on QM cluster models of S-adenosyl-L-methionine-dependent methyltransferases ("},"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":"2607.01362","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"physics.chem-ph","submitted_at":"2026-07-01T18:24:10Z","cross_cats_sorted":["cs.LG","q-bio.BM"],"title_canon_sha256":"084f39f6fcc842d0ec84e4a0d303048fb3189e5b2f125d3288bc33bf82801e2a","abstract_canon_sha256":"7d9cc85d3faca683aaad023cab83f774b3a5218b21e9ef99e9946585fc9ab9b2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-03T00:16:58.328539Z","signature_b64":"hkbyk7OK9HU8tPKw9FgDBeLMwrJZ7wAdyVrUGmMCjuOWp3hf+Z65DeuotaCkZz5cEjzkDMuL8VyOKBt6AUi9BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"71002cccc283a41e10eb3dcef697032f71ca101e68acaa686ad1995cafe7b12b","last_reissued_at":"2026-07-03T00:16:58.328124Z","signature_status":"signed_v1","first_computed_at":"2026-07-03T00:16:58.328124Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","q-bio.BM"],"primary_cat":"physics.chem-ph","authors_text":"Heather J. Kulik, Weiliang Luo","submitted_at":"2026-07-01T18:24:10Z","abstract_excerpt":"Quantum mechanical (QM) cluster models provide an effective framework for mechanistic studies of enzymatic reactions but remain computationally demanding. Neural network potentials (NNPs) offer a promising route to reduce this cost, but enzymes present challenges beyond small molecules, including large system sizes, implicit-solvent environments, substantial polarization, and charge transfer. Here, we present an integrated software framework for efficient NNP training for mechanistic studies of enzymes, demonstrated on QM cluster models of S-adenosyl-L-methionine-dependent methyltransferases ("},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.01362","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/2607.01362/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":"2607.01362","created_at":"2026-07-03T00:16:58.328193+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.01362v1","created_at":"2026-07-03T00:16:58.328193+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.01362","created_at":"2026-07-03T00:16:58.328193+00:00"},{"alias_kind":"pith_short_12","alias_value":"OEACZTGCQOSB","created_at":"2026-07-03T00:16:58.328193+00:00"},{"alias_kind":"pith_short_16","alias_value":"OEACZTGCQOSB4EHL","created_at":"2026-07-03T00:16:58.328193+00:00"},{"alias_kind":"pith_short_8","alias_value":"OEACZTGC","created_at":"2026-07-03T00:16:58.328193+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/OEACZTGCQOSB4EHLHXHPNFYDF5","json":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5.json","graph_json":"https://pith.science/api/pith-number/OEACZTGCQOSB4EHLHXHPNFYDF5/graph.json","events_json":"https://pith.science/api/pith-number/OEACZTGCQOSB4EHLHXHPNFYDF5/events.json","paper":"https://pith.science/paper/OEACZTGC"},"agent_actions":{"view_html":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5","download_json":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5.json","view_paper":"https://pith.science/paper/OEACZTGC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.01362&json=true","fetch_graph":"https://pith.science/api/pith-number/OEACZTGCQOSB4EHLHXHPNFYDF5/graph.json","fetch_events":"https://pith.science/api/pith-number/OEACZTGCQOSB4EHLHXHPNFYDF5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5/action/storage_attestation","attest_author":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5/action/author_attestation","sign_citation":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5/action/citation_signature","submit_replication":"https://pith.science/pith/OEACZTGCQOSB4EHLHXHPNFYDF5/action/replication_record"}},"created_at":"2026-07-03T00:16:58.328193+00:00","updated_at":"2026-07-03T00:16:58.328193+00:00"}