{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YAGOGRBOGQRC4HI7XC2LLEGX6A","short_pith_number":"pith:YAGOGRBO","canonical_record":{"source":{"id":"1805.00108","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-30T21:36:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"eb2bd943ff4de9294688825fed032d07469809f7329d2f095adb447d5cad9d37","abstract_canon_sha256":"c9253a0c1d96a3fcbd3f53afc8595149aaafac93931505c4f2b09a434efb805d"},"schema_version":"1.0"},"canonical_sha256":"c00ce3442e34222e1d1fb8b4b590d7f019cfe719b83e5a92ffa3176d025db077","source":{"kind":"arxiv","id":"1805.00108","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.00108","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"arxiv_version","alias_value":"1805.00108v3","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.00108","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"pith_short_12","alias_value":"YAGOGRBOGQRC","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YAGOGRBOGQRC4HI7","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YAGOGRBO","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YAGOGRBOGQRC4HI7XC2LLEGX6A","target":"record","payload":{"canonical_record":{"source":{"id":"1805.00108","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-30T21:36:05Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"eb2bd943ff4de9294688825fed032d07469809f7329d2f095adb447d5cad9d37","abstract_canon_sha256":"c9253a0c1d96a3fcbd3f53afc8595149aaafac93931505c4f2b09a434efb805d"},"schema_version":"1.0"},"canonical_sha256":"c00ce3442e34222e1d1fb8b4b590d7f019cfe719b83e5a92ffa3176d025db077","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:52.934632Z","signature_b64":"UPh5JZMHzybl+E5V8zVuMeus2cN13wOgwYIsQaJqT+WN3P7QgY0GCHLwXCnNj6Fgy2Tt1lu616+p8q8u7hTrAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c00ce3442e34222e1d1fb8b4b590d7f019cfe719b83e5a92ffa3176d025db077","last_reissued_at":"2026-05-17T23:49:52.934257Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:52.934257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.00108","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AEHGDxtPWMyAi+Qrg7hinoMC8xWx2T7lqiL0lolYR+YCMmm0Z1DEYfV/kh5xTUv9DZpfVF1VWpkpg8CCRg2NAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T22:17:26.926188Z"},"content_sha256":"4aef81c349c4e4daef59f30ca84d1374d6a164d555c78e40959b9c830a87a9d5","schema_version":"1.0","event_id":"sha256:4aef81c349c4e4daef59f30ca84d1374d6a164d555c78e40959b9c830a87a9d5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YAGOGRBOGQRC4HI7XC2LLEGX6A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Conditional molecular design with deep generative models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Kyunghyun Cho, Seokho Kang","submitted_at":"2018-04-30T21:36:05Z","abstract_excerpt":"Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by samplin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.00108","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"mdrXDtifATMWIZ+PSOgzeJWfP3JpeNhB0HRPGBzR8ClbcuEEWlNGbGFTfPkeNCH9v17J89Lpnj0bYTeBlbAJDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-21T22:17:26.926825Z"},"content_sha256":"416b87ee2efadf9c49a7b2c8ced5e9e4449fa16f4810dd0520240d724e6b771c","schema_version":"1.0","event_id":"sha256:416b87ee2efadf9c49a7b2c8ced5e9e4449fa16f4810dd0520240d724e6b771c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/bundle.json","state_url":"https://pith.science/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/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":"2026-05-21T22:17:26Z","links":{"resolver":"https://pith.science/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A","bundle":"https://pith.science/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/bundle.json","state":"https://pith.science/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YAGOGRBOGQRC4HI7XC2LLEGX6A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YAGOGRBOGQRC4HI7XC2LLEGX6A","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":"c9253a0c1d96a3fcbd3f53afc8595149aaafac93931505c4f2b09a434efb805d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-30T21:36:05Z","title_canon_sha256":"eb2bd943ff4de9294688825fed032d07469809f7329d2f095adb447d5cad9d37"},"schema_version":"1.0","source":{"id":"1805.00108","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.00108","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"arxiv_version","alias_value":"1805.00108v3","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.00108","created_at":"2026-05-17T23:49:52Z"},{"alias_kind":"pith_short_12","alias_value":"YAGOGRBOGQRC","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YAGOGRBOGQRC4HI7","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YAGOGRBO","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:416b87ee2efadf9c49a7b2c8ced5e9e4449fa16f4810dd0520240d724e6b771c","target":"graph","created_at":"2026-05-17T23:49:52Z","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"},"paper":{"abstract_excerpt":"Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semi-supervised variational autoencoder trained on a set of existing molecules with only a partial annotation. We generate new molecules with desired properties by samplin","authors_text":"Kyunghyun Cho, Seokho Kang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-30T21:36:05Z","title":"Conditional molecular design with deep generative models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.00108","kind":"arxiv","version":3},"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":"sha256:4aef81c349c4e4daef59f30ca84d1374d6a164d555c78e40959b9c830a87a9d5","target":"record","created_at":"2026-05-17T23:49:52Z","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":"c9253a0c1d96a3fcbd3f53afc8595149aaafac93931505c4f2b09a434efb805d","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-04-30T21:36:05Z","title_canon_sha256":"eb2bd943ff4de9294688825fed032d07469809f7329d2f095adb447d5cad9d37"},"schema_version":"1.0","source":{"id":"1805.00108","kind":"arxiv","version":3}},"canonical_sha256":"c00ce3442e34222e1d1fb8b4b590d7f019cfe719b83e5a92ffa3176d025db077","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c00ce3442e34222e1d1fb8b4b590d7f019cfe719b83e5a92ffa3176d025db077","first_computed_at":"2026-05-17T23:49:52.934257Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:52.934257Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UPh5JZMHzybl+E5V8zVuMeus2cN13wOgwYIsQaJqT+WN3P7QgY0GCHLwXCnNj6Fgy2Tt1lu616+p8q8u7hTrAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:52.934632Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.00108","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4aef81c349c4e4daef59f30ca84d1374d6a164d555c78e40959b9c830a87a9d5","sha256:416b87ee2efadf9c49a7b2c8ced5e9e4449fa16f4810dd0520240d724e6b771c"],"state_sha256":"ca892e2fc181351da08633794c9d75f7ebb44cc01658ac9e05f9e2669758400a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zRcNH/l8GDYY5hSNzpgK9+OQIarooWNKReASetJrwkjX582Ui3TmsWFTdu2zRSdbLvkp1koz6G5pqNgZpwflBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-21T22:17:26.930014Z","bundle_sha256":"28ebbeeb4a9d2c6ede8676e06e9c479535ddad2465d4a51615395b9cd041a28e"}}