{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:FB55XOBQ3H5IFTYNA4Q2ZG3LIQ","short_pith_number":"pith:FB55XOBQ","canonical_record":{"source":{"id":"1701.01329","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-01-05T14:28:34Z","cross_cats_sorted":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"title_canon_sha256":"d6b4288d9c400ebb4998ce1f9f5dd32e8c6fff0c22dd00319b602e9a0fb10d15","abstract_canon_sha256":"e0f14d18a24c9b87d82edb789d5ad69eec953b043ad8954dcb11b1b247e9ff7b"},"schema_version":"1.0"},"canonical_sha256":"287bdbb830d9fa82cf0d0721ac9b6b4412a94372a19bdb678d9303273a11236a","source":{"kind":"arxiv","id":"1701.01329","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.01329","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"arxiv_version","alias_value":"1701.01329v1","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.01329","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"pith_short_12","alias_value":"FB55XOBQ3H5I","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FB55XOBQ3H5IFTYN","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FB55XOBQ","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:FB55XOBQ3H5IFTYNA4Q2ZG3LIQ","target":"record","payload":{"canonical_record":{"source":{"id":"1701.01329","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-01-05T14:28:34Z","cross_cats_sorted":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"title_canon_sha256":"d6b4288d9c400ebb4998ce1f9f5dd32e8c6fff0c22dd00319b602e9a0fb10d15","abstract_canon_sha256":"e0f14d18a24c9b87d82edb789d5ad69eec953b043ad8954dcb11b1b247e9ff7b"},"schema_version":"1.0"},"canonical_sha256":"287bdbb830d9fa82cf0d0721ac9b6b4412a94372a19bdb678d9303273a11236a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:53:19.883142Z","signature_b64":"L1WlbmG3bVhBoE5kimiSSoraUTolJYx/Ha+49MHEH0wYShOkG11limqp1ILxthVb/YXURH70voqWB9IsfgsmCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"287bdbb830d9fa82cf0d0721ac9b6b4412a94372a19bdb678d9303273a11236a","last_reissued_at":"2026-05-18T00:53:19.882747Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:53:19.882747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1701.01329","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:53:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"U2Jo7jzt2pDyUKqjn4zkMDeNZfDBpEFlQWKZMdhT0z7oAA4SzWESytE5yMKK4GyQbp1AQWSojolAUtzpWl+tDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T10:07:28.683050Z"},"content_sha256":"b95a794daa7c1889e4aaea6765937951eb113eca51826376f627e0d7fd77dd6c","schema_version":"1.0","event_id":"sha256:b95a794daa7c1889e4aaea6765937951eb113eca51826376f627e0d7fd77dd6c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:FB55XOBQ3H5IFTYNA4Q2ZG3LIQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"primary_cat":"cs.NE","authors_text":"Christian Tyrchan, Mark P. Waller, Marwin H.S. Segler, Thierry Kogej","submitted_at":"2017-01-05T14:28:34Z","abstract_excerpt":"In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.01329","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":""},"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":"2026-05-18T00:53:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VYNCIJxuEBgNgpo8fHOd70OA5J8SO+y1LU8+9w0Md/aWGrRlMSWAJFrNiU67WSB+onRAm3h/P7NVEpUmaS+eBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T10:07:28.683674Z"},"content_sha256":"b1e61fc4869013a7ce1dbb42b4fc6ecac7a1fccc948d5b62e5f6046e954fccf2","schema_version":"1.0","event_id":"sha256:b1e61fc4869013a7ce1dbb42b4fc6ecac7a1fccc948d5b62e5f6046e954fccf2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/bundle.json","state_url":"https://pith.science/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/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":"2026-06-06T10:07:28Z","links":{"resolver":"https://pith.science/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ","bundle":"https://pith.science/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/bundle.json","state":"https://pith.science/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FB55XOBQ3H5IFTYNA4Q2ZG3LIQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:FB55XOBQ3H5IFTYNA4Q2ZG3LIQ","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":"e0f14d18a24c9b87d82edb789d5ad69eec953b043ad8954dcb11b1b247e9ff7b","cross_cats_sorted":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-01-05T14:28:34Z","title_canon_sha256":"d6b4288d9c400ebb4998ce1f9f5dd32e8c6fff0c22dd00319b602e9a0fb10d15"},"schema_version":"1.0","source":{"id":"1701.01329","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1701.01329","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"arxiv_version","alias_value":"1701.01329v1","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1701.01329","created_at":"2026-05-18T00:53:19Z"},{"alias_kind":"pith_short_12","alias_value":"FB55XOBQ3H5I","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"FB55XOBQ3H5IFTYN","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"FB55XOBQ","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:b1e61fc4869013a7ce1dbb42b4fc6ecac7a1fccc948d5b62e5f6046e954fccf2","target":"graph","created_at":"2026-05-18T00:53:19Z","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":"In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active towards a given biological target, we propose to fine-tune the model with small ","authors_text":"Christian Tyrchan, Mark P. Waller, Marwin H.S. Segler, Thierry Kogej","cross_cats":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-01-05T14:28:34Z","title":"Generating Focussed Molecule Libraries for Drug Discovery with Recurrent Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1701.01329","kind":"arxiv","version":1},"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:b95a794daa7c1889e4aaea6765937951eb113eca51826376f627e0d7fd77dd6c","target":"record","created_at":"2026-05-18T00:53:19Z","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":"e0f14d18a24c9b87d82edb789d5ad69eec953b043ad8954dcb11b1b247e9ff7b","cross_cats_sorted":["cs.AI","cs.LG","physics.chem-ph","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2017-01-05T14:28:34Z","title_canon_sha256":"d6b4288d9c400ebb4998ce1f9f5dd32e8c6fff0c22dd00319b602e9a0fb10d15"},"schema_version":"1.0","source":{"id":"1701.01329","kind":"arxiv","version":1}},"canonical_sha256":"287bdbb830d9fa82cf0d0721ac9b6b4412a94372a19bdb678d9303273a11236a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"287bdbb830d9fa82cf0d0721ac9b6b4412a94372a19bdb678d9303273a11236a","first_computed_at":"2026-05-18T00:53:19.882747Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:53:19.882747Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"L1WlbmG3bVhBoE5kimiSSoraUTolJYx/Ha+49MHEH0wYShOkG11limqp1ILxthVb/YXURH70voqWB9IsfgsmCw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:53:19.883142Z","signed_message":"canonical_sha256_bytes"},"source_id":"1701.01329","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b95a794daa7c1889e4aaea6765937951eb113eca51826376f627e0d7fd77dd6c","sha256:b1e61fc4869013a7ce1dbb42b4fc6ecac7a1fccc948d5b62e5f6046e954fccf2"],"state_sha256":"96209ec5695f95b1207069acdcdc52fc60fb536c4768af21d7ce9b5f4a0fce5c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"vStzZ5lWEQpQ3GHmsntppsiHUnbtClKq1kH5IGarjHRbr8VyHEPdrzmMP7CjKInxd0auqZtWW3sOPNeac+1HCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T10:07:28.687682Z","bundle_sha256":"b0bb2ead9a692c8b49733f606707a074dead54f6cc677bff6895dcf31f593d6d"}}