{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:74Y6SGJKY45QXSGKTBC7NNBXTI","short_pith_number":"pith:74Y6SGJK","schema_version":"1.0","canonical_sha256":"ff31e9192ac73b0bc8ca9845f6b4379a18f2162f08273d1f65c4bfead79432ec","source":{"kind":"arxiv","id":"1809.02745","version":2},"attestation_state":"computed","paper":{"title":"Molecular Hypergraph Grammar with its Application to Molecular Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hiroshi Kajino","submitted_at":"2018-09-08T02:25:52Z","abstract_excerpt":"Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a "},"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":"1809.02745","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-09-08T02:25:52Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"1472f97e6cc79006b7390e4962f8e6c3da60d5a25b4c91bf61d202569f477f85","abstract_canon_sha256":"d00b1506f209a0c5003d9adedc45d303b65f1f198e42f94937980462fb739269"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:47:59.569818Z","signature_b64":"mmYRK6UlPdroM8fZb9dhNukfErU7z3p74+pqMC45mGnEREt73kwx7qkxng0ijoqvZmySVeLLBnbhr9L5UqHNAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff31e9192ac73b0bc8ca9845f6b4379a18f2162f08273d1f65c4bfead79432ec","last_reissued_at":"2026-05-17T23:47:59.569420Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:47:59.569420Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Molecular Hypergraph Grammar with its Application to Molecular Optimization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Hiroshi Kajino","submitted_at":"2018-09-08T02:25:52Z","abstract_excerpt":"Molecular optimization aims to discover novel molecules with desirable properties. Two fundamental challenges are: (i) it is not trivial to generate valid molecules in a controllable way due to hard chemical constraints such as the valency conditions, and (ii) it is often costly to evaluate a property of a novel molecule, and therefore, the number of property evaluations is limited. These challenges are to some extent alleviated by a combination of a variational autoencoder (VAE) and Bayesian optimization (BO). VAE converts a molecule into/from its latent continuous vector, and BO optimizes a "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1809.02745","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"1809.02745","created_at":"2026-05-17T23:47:59.569478+00:00"},{"alias_kind":"arxiv_version","alias_value":"1809.02745v2","created_at":"2026-05-17T23:47:59.569478+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1809.02745","created_at":"2026-05-17T23:47:59.569478+00:00"},{"alias_kind":"pith_short_12","alias_value":"74Y6SGJKY45Q","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_16","alias_value":"74Y6SGJKY45QXSGK","created_at":"2026-05-18T12:32:11.075285+00:00"},{"alias_kind":"pith_short_8","alias_value":"74Y6SGJK","created_at":"2026-05-18T12:32:11.075285+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/74Y6SGJKY45QXSGKTBC7NNBXTI","json":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI.json","graph_json":"https://pith.science/api/pith-number/74Y6SGJKY45QXSGKTBC7NNBXTI/graph.json","events_json":"https://pith.science/api/pith-number/74Y6SGJKY45QXSGKTBC7NNBXTI/events.json","paper":"https://pith.science/paper/74Y6SGJK"},"agent_actions":{"view_html":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI","download_json":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI.json","view_paper":"https://pith.science/paper/74Y6SGJK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1809.02745&json=true","fetch_graph":"https://pith.science/api/pith-number/74Y6SGJKY45QXSGKTBC7NNBXTI/graph.json","fetch_events":"https://pith.science/api/pith-number/74Y6SGJKY45QXSGKTBC7NNBXTI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI/action/storage_attestation","attest_author":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI/action/author_attestation","sign_citation":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI/action/citation_signature","submit_replication":"https://pith.science/pith/74Y6SGJKY45QXSGKTBC7NNBXTI/action/replication_record"}},"created_at":"2026-05-17T23:47:59.569478+00:00","updated_at":"2026-05-17T23:47:59.569478+00:00"}