{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:OPFFKM2SXPRSTQXM2YFCJ6CPYH","short_pith_number":"pith:OPFFKM2S","schema_version":"1.0","canonical_sha256":"73ca553352bbe329c2ecd60a24f84fc1d6032b8e92f8f1ef942b576bf474c00c","source":{"kind":"arxiv","id":"2210.16823","version":3},"attestation_state":"computed","paper":{"title":"Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alan Aspuru-Guzik, Alex Aliper, Alex Zhavoronkov, Feng Ren, Jen-Yueh Hsiao, Min-Hsiu Hsieh, Po-Yu Kao, Wei-Yin Chiang, Ya-Chu Yang, Yen-Chu Lin, Yudong Cao","submitted_at":"2022-10-30T11:57:56Z","abstract_excerpt":"De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly-developing fields with lead candidate discovery in the drug development process. First, Artificial intelligence has already been demonstrated"},"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":"2210.16823","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"quant-ph","submitted_at":"2022-10-30T11:57:56Z","cross_cats_sorted":[],"title_canon_sha256":"ad201f7d2562d0268c41d10fbe3adf401c77b01ece09110883c0f675ee51a531","abstract_canon_sha256":"41221964b1873bc87820aadd91910b00742cde72de113c30f16cc135a5bab944"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:09:20.400967Z","signature_b64":"reVo2QaVi1jY6is0M26GQg9gkyEbGWxQpFK1LtvpSjC9pMx38rUEGI1WQL4ZwaBIyIWG8HxVZoqRzhQPM2CUDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"73ca553352bbe329c2ecd60a24f84fc1d6032b8e92f8f1ef942b576bf474c00c","last_reissued_at":"2026-07-05T06:09:20.400563Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:09:20.400563Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploring the Advantages of Quantum Generative Adversarial Networks in Generative Chemistry","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"quant-ph","authors_text":"Alan Aspuru-Guzik, Alex Aliper, Alex Zhavoronkov, Feng Ren, Jen-Yueh Hsiao, Min-Hsiu Hsieh, Po-Yu Kao, Wei-Yin Chiang, Ya-Chu Yang, Yen-Chu Lin, Yudong Cao","submitted_at":"2022-10-30T11:57:56Z","abstract_excerpt":"De novo drug design with desired biological activities is crucial for developing novel therapeutics for patients. The drug development process is time and resource-consuming, and it has a low probability of success. Recent advances in machine learning and deep learning technology have reduced the time and cost of the discovery process and therefore, improved pharmaceutical research and development. In this paper, we explore the combination of two rapidly-developing fields with lead candidate discovery in the drug development process. First, Artificial intelligence has already been demonstrated"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.16823","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2210.16823/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":"2210.16823","created_at":"2026-07-05T06:09:20.400622+00:00"},{"alias_kind":"arxiv_version","alias_value":"2210.16823v3","created_at":"2026-07-05T06:09:20.400622+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.16823","created_at":"2026-07-05T06:09:20.400622+00:00"},{"alias_kind":"pith_short_12","alias_value":"OPFFKM2SXPRS","created_at":"2026-07-05T06:09:20.400622+00:00"},{"alias_kind":"pith_short_16","alias_value":"OPFFKM2SXPRSTQXM","created_at":"2026-07-05T06:09:20.400622+00:00"},{"alias_kind":"pith_short_8","alias_value":"OPFFKM2S","created_at":"2026-07-05T06:09:20.400622+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/OPFFKM2SXPRSTQXM2YFCJ6CPYH","json":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH.json","graph_json":"https://pith.science/api/pith-number/OPFFKM2SXPRSTQXM2YFCJ6CPYH/graph.json","events_json":"https://pith.science/api/pith-number/OPFFKM2SXPRSTQXM2YFCJ6CPYH/events.json","paper":"https://pith.science/paper/OPFFKM2S"},"agent_actions":{"view_html":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH","download_json":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH.json","view_paper":"https://pith.science/paper/OPFFKM2S","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2210.16823&json=true","fetch_graph":"https://pith.science/api/pith-number/OPFFKM2SXPRSTQXM2YFCJ6CPYH/graph.json","fetch_events":"https://pith.science/api/pith-number/OPFFKM2SXPRSTQXM2YFCJ6CPYH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH/action/storage_attestation","attest_author":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH/action/author_attestation","sign_citation":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH/action/citation_signature","submit_replication":"https://pith.science/pith/OPFFKM2SXPRSTQXM2YFCJ6CPYH/action/replication_record"}},"created_at":"2026-07-05T06:09:20.400622+00:00","updated_at":"2026-07-05T06:09:20.400622+00:00"}