{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:5CKIRF5B2L65GKHDIL5WSLSBRR","short_pith_number":"pith:5CKIRF5B","schema_version":"1.0","canonical_sha256":"e8948897a1d2fdd328e342fb692e418c541d41c3176428dbad1b475570428449","source":{"kind":"arxiv","id":"2003.12725","version":3},"attestation_state":"computed","paper":{"title":"A Graph to Graphs Framework for Retrosynthesis Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chence Shi, Hongyu Guo, Jian Tang, Ming Zhang, Minkai Xu","submitted_at":"2020-03-28T06:16:56Z","abstract_excerpt":"A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, "},"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":"2003.12725","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2020-03-28T06:16:56Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"bfe01cef218d36bb06ce48472ecc4dbb6797cf52c6d72804642ed09720fb488e","abstract_canon_sha256":"067392b002390885e960a66edc58e234ccbc53e8bb4be83c48e2dd95b380cb35"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:07:18.744462Z","signature_b64":"c7eC0T91wo32X3tlmkabn9J9n/4lk5cxdjsSrs//wOc4+2yCt/wznjdeUHQiA9Ok/f3eWJp1DFkO0gNPY+TEAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e8948897a1d2fdd328e342fb692e418c541d41c3176428dbad1b475570428449","last_reissued_at":"2026-07-05T03:07:18.743971Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:07:18.743971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Graph to Graphs Framework for Retrosynthesis Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Chence Shi, Hongyu Guo, Jian Tang, Ming Zhang, Minkai Xu","submitted_at":"2020-03-28T06:16:56Z","abstract_excerpt":"A fundamental problem in computational chemistry is to find a set of reactants to synthesize a target molecule, a.k.a. retrosynthesis prediction. Existing state-of-the-art methods rely on matching the target molecule with a large set of reaction templates, which are very computationally expensive and also suffer from the problem of coverage. In this paper, we propose a novel template-free approach called G2Gs by transforming a target molecular graph into a set of reactant molecular graphs. G2Gs first splits the target molecular graph into a set of synthons by identifying the reaction centers, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2003.12725","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/2003.12725/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":"2003.12725","created_at":"2026-07-05T03:07:18.744060+00:00"},{"alias_kind":"arxiv_version","alias_value":"2003.12725v3","created_at":"2026-07-05T03:07:18.744060+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2003.12725","created_at":"2026-07-05T03:07:18.744060+00:00"},{"alias_kind":"pith_short_12","alias_value":"5CKIRF5B2L65","created_at":"2026-07-05T03:07:18.744060+00:00"},{"alias_kind":"pith_short_16","alias_value":"5CKIRF5B2L65GKHD","created_at":"2026-07-05T03:07:18.744060+00:00"},{"alias_kind":"pith_short_8","alias_value":"5CKIRF5B","created_at":"2026-07-05T03:07:18.744060+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2411.19503","citing_title":"Hierarchical Framework for Retrosynthesis Prediction with Enhanced Reaction Center Localization","ref_index":21,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR","json":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR.json","graph_json":"https://pith.science/api/pith-number/5CKIRF5B2L65GKHDIL5WSLSBRR/graph.json","events_json":"https://pith.science/api/pith-number/5CKIRF5B2L65GKHDIL5WSLSBRR/events.json","paper":"https://pith.science/paper/5CKIRF5B"},"agent_actions":{"view_html":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR","download_json":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR.json","view_paper":"https://pith.science/paper/5CKIRF5B","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2003.12725&json=true","fetch_graph":"https://pith.science/api/pith-number/5CKIRF5B2L65GKHDIL5WSLSBRR/graph.json","fetch_events":"https://pith.science/api/pith-number/5CKIRF5B2L65GKHDIL5WSLSBRR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR/action/storage_attestation","attest_author":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR/action/author_attestation","sign_citation":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR/action/citation_signature","submit_replication":"https://pith.science/pith/5CKIRF5B2L65GKHDIL5WSLSBRR/action/replication_record"}},"created_at":"2026-07-05T03:07:18.744060+00:00","updated_at":"2026-07-05T03:07:18.744060+00:00"}