{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:OOKREJY4CR4IXET2RZFS4LRBNE","short_pith_number":"pith:OOKREJY4","schema_version":"1.0","canonical_sha256":"739512271c14788b927a8e4b2e2e2169048537824477c1faffb93729dbfbea6b","source":{"kind":"arxiv","id":"2411.05019","version":1},"attestation_state":"computed","paper":{"title":"Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"ChiYung Yam, Guodong Du, Ho-Kin Tang, Mingjun Han, Taotao Yu, Yukai Zhang","submitted_at":"2024-10-24T09:13:20Z","abstract_excerpt":"The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yield"},"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":"2411.05019","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.chem-ph","submitted_at":"2024-10-24T09:13:20Z","cross_cats_sorted":["physics.comp-ph"],"title_canon_sha256":"3eb6adae9079d4e380797309ffc94e1a7ae2d8ac14636ee4639c3c61bd765dc3","abstract_canon_sha256":"7dffe5896fe99cab5a2cebc426037c3b75e06283bdb3754c7317370d1b0f8413"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:32:50.860563Z","signature_b64":"s1qihZb/4xohNZVtrxWFkY+zwwp/aSmwehN0YprAUq79E55Bq5mGUgI70O+2a7VXb4S56spctkQNV7xv6d+SCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"739512271c14788b927a8e4b2e2e2169048537824477c1faffb93729dbfbea6b","last_reissued_at":"2026-07-05T09:32:50.860092Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:32:50.860092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhancing Accuracy and Feature Insights in Hydration Free Energy Predictions for Small Molecules with Machine Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["physics.comp-ph"],"primary_cat":"physics.chem-ph","authors_text":"ChiYung Yam, Guodong Du, Ho-Kin Tang, Mingjun Han, Taotao Yu, Yukai Zhang","submitted_at":"2024-10-24T09:13:20Z","abstract_excerpt":"The accurate prediction of solvation free energy is of significant importance as it governs the behavior of solutes in solution. In this work, we apply a variety of machine learning techniques to predict and analyze the alchemical free energy of small molecules. Our methodology incorporates an ensemble of machine learning models with feature processing using the K-nearest neighbors algorithm. Two training strategies are explored: one based on experimental data, and the other based on the offset between molecular dynamics (MD) simulations and experimental measurements. The latter approach yield"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.05019","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/2411.05019/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":"2411.05019","created_at":"2026-07-05T09:32:50.860155+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.05019v1","created_at":"2026-07-05T09:32:50.860155+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.05019","created_at":"2026-07-05T09:32:50.860155+00:00"},{"alias_kind":"pith_short_12","alias_value":"OOKREJY4CR4I","created_at":"2026-07-05T09:32:50.860155+00:00"},{"alias_kind":"pith_short_16","alias_value":"OOKREJY4CR4IXET2","created_at":"2026-07-05T09:32:50.860155+00:00"},{"alias_kind":"pith_short_8","alias_value":"OOKREJY4","created_at":"2026-07-05T09:32:50.860155+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/OOKREJY4CR4IXET2RZFS4LRBNE","json":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE.json","graph_json":"https://pith.science/api/pith-number/OOKREJY4CR4IXET2RZFS4LRBNE/graph.json","events_json":"https://pith.science/api/pith-number/OOKREJY4CR4IXET2RZFS4LRBNE/events.json","paper":"https://pith.science/paper/OOKREJY4"},"agent_actions":{"view_html":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE","download_json":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE.json","view_paper":"https://pith.science/paper/OOKREJY4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.05019&json=true","fetch_graph":"https://pith.science/api/pith-number/OOKREJY4CR4IXET2RZFS4LRBNE/graph.json","fetch_events":"https://pith.science/api/pith-number/OOKREJY4CR4IXET2RZFS4LRBNE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE/action/storage_attestation","attest_author":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE/action/author_attestation","sign_citation":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE/action/citation_signature","submit_replication":"https://pith.science/pith/OOKREJY4CR4IXET2RZFS4LRBNE/action/replication_record"}},"created_at":"2026-07-05T09:32:50.860155+00:00","updated_at":"2026-07-05T09:32:50.860155+00:00"}