{"as_of":"2026-08-23T10:04:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:70db2f3c006d2b2ae58879ca7dfcca23a1d3609bf1abfa64c41c483ac8e227c2","coverage":[{"denominator":66,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":66,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T21:54:56.246937Z","state":"measured"},{"denominator":66,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":66,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-23T06:30:58.430688+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/2505.08688/citation-record","integrity":"/paper/2505.08688/integrity","json":"/paper/2505.08688/citation-record.json","paper":"/paper/2505.08688"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.724608Z","title":"Molecular representations in AI -driven drug discovery: a review and practical guide","venue":null,"work_id":"e0e8f992-7761-46a3-a398-61896b45a06e","year":2020},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:55.975561Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:e2552a705445fef3c19e8086422c57bb9020b366606585377fe2680cbf69493d","observation_id":"c85481f8-8bf8-4ae1-859a-3a530d7d9178","resolution":{"observed_at":"2026-08-15T21:54:57.728894Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.708818Z","title":"P.; Kulik, H","venue":null,"work_id":"02d2401f-56ea-4ec1-ad1e-5ee6d3790c24","year":2020},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:55.982169Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:19811d0cf79e9f1e26a5695971802b7c760dff4f3617943c340c0661e9cec0f5","observation_id":"0ae657d9-1167-45fa-96d4-fc7e1d176688","resolution":{"observed_at":"2026-08-15T21:54:57.715037Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.691472Z","title":null,"venue":null,"work_id":"4c5c0603-bc80-4b3a-8565-ecae9f51ec5d","year":2020},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:55.987673Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:01e50be26167c26a6411a13d19c4760d0381f9325db2d610385d5a6cef3abd16","observation_id":"79a813a4-9ec9-4d8b-8017-963a499eb5e7","resolution":{"observed_at":"2026-08-15T21:54:57.697526Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.669127Z","title":"Internally Consistent Prediction of Vapor Pressure and Related Properties","venue":null,"work_id":"e9485dbc-9868-4e35-b1e6-efdbe6d22223","year":2000},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:55.993091Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:59d5c38704e2fef31fa01063da14e7072876d082249da8268e6d6d7d9ff2b3a2","observation_id":"e5cda886-19bc-40ed-bf6e-2268dd5393f2","resolution":{"observed_at":"2026-08-15T21:54:57.673970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.654759Z","title":null,"venue":null,"work_id":"29ae6dfb-ec05-4a48-b083-8d107841420a","year":1973},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:55.998487Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:8985c07e5a64130cdcdfa6185ebc93571c91baf920f91520395513236d75ec0f","observation_id":"006cd546-a954-4fd4-a41d-a569bc6cd8f7","resolution":{"observed_at":"2026-08-15T21:54:57.659760Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.641034Z","title":"J.; Holder, G","venue":null,"work_id":"db8561d8-cbaa-4bad-8bfa-56b280ae0072","year":1997},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.002818Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:080ca01091763036ec910abf10d30cbe4c6c1daae858d5bc8e8defae708b8536","observation_id":"65f3f634-b094-44e0-9660-fe93cb354cea","resolution":{"observed_at":"2026-08-15T21:54:57.645016Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.630846Z","title":"Prediction of Fluid Phase Behavior from Molecular Models","venue":null,"work_id":"d687ef03-f532-48e1-ac5d-5a0b9d84d65d","year":2007},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.007472Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:62941bd7ec90d70f9cf31d751979466d401571a85c4ef52b5bf9fc6078cf9168","observation_id":"1f00f6a1-70bf-417a-84e2-3855378571f4","resolution":{"observed_at":"2026-08-15T21:54:57.633690Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.620376Z","title":"S.; Hilal, S","venue":null,"work_id":"8aa3f0b8-f66f-4b7b-859a-b7ebcb86c6a1","year":2016},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.011423Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:589330a87afa4ffb77a329011fd30d6e7f30ac292c8ba311244b239b021e63c0","observation_id":"f2e186a8-be0d-4190-843c-f464678fe8fc","resolution":{"observed_at":"2026-08-15T21:54:57.623689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.609512Z","title":null,"venue":null,"work_id":"f3134898-7698-4996-80b3-9c8fa28741bc","year":2003},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.015647Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:06cd1d01f6fdb582e6d8fbffbb748bf36a261c33a251e8660cc732c771f4d63c","observation_id":"f5facb1d-758f-4514-baf0-627765a0c5dc","resolution":{"observed_at":"2026-08-15T21:54:57.612683Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.593771Z","title":"W.; Craighead, J","venue":null,"work_id":"9933e8eb-d843-4c08-a6e9-d2a9d76bcb05","year":2014},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.019675Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:bad9db3dd7e6362734540e03d533fe697c64785d2f42f0b9e76c922ee21e6393","observation_id":"15f66974-c6f5-4624-814a-131ba41f0258","resolution":{"observed_at":"2026-08-15T21:54:57.599735Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.573570Z","title":"Trends in clinical success rates and therapeutic focus","venue":null,"work_id":"06704b94-8ee3-42a5-bfbc-bb643d0cfa39","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.023574Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:fdbc2f1507c61f567a4f478ff577538787c5b68cdf4717b0541a55d08d89ec0c","observation_id":"6ab26968-997d-4701-a203-2fea22ab361e","resolution":{"observed_at":"2026-08-15T21:54:57.578241Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.555588Z","title":"Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition","venue":null,"work_id":"c684d342-42ee-4562-82eb-36215ec9ae2b","year":2018},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.028425Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:33244002c1a85fd59dfcdf44210c58c7aa0bed85a1b748f7afaad13aa5916b9a","observation_id":"8617d3cd-4413-46b8-9876-24738b225fd5","resolution":{"observed_at":"2026-08-15T21:54:57.560989Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"records/7559628","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.616872Z","title":null,"venue":null,"work_id":"a2b6ce00-352f-49a7-802b-3d842375ad26","year":2021},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.035303Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:79bb7154d4ffad1e2b65c4333acb8002e0b30f90653fba4e0f2f39825e33b451","observation_id":"07fdca65-d474-429a-913f-5e371f40c415","resolution":{"observed_at":"2026-08-15T21:54:56.623721Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.541837Z","title":"N.; Remijan, A","venue":null,"work_id":"56aa9bf1-e69e-4cba-a26d-447a490b4e18","year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.040708Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:64c3c783d4dd84b78d3dec6ff6ba0f3bad8287554d834d134c837e17821bd2db","observation_id":"4d0ff1a4-8887-4476-94a2-5e78516a3dc3","resolution":{"observed_at":"2026-08-15T21:54:57.547464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.521530Z","title":null,"venue":null,"work_id":"3ce65621-c672-4752-9a2c-e300afcdb0d8","year":2021},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.044669Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:0e415117c00b1f8e21780a4a1527d1689e549b9e40bfeff51461f823bf369b6d","observation_id":"810e7725-f2de-4a07-964c-076bdd03a486","resolution":{"observed_at":"2026-08-15T21:54:57.529611Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.507462Z","title":null,"venue":null,"work_id":"b3da937c-2007-47c9-bbeb-2f8cad48f965","year":2001},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.048568Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f6a5809b5e9aed8596199ac23ec02de9322717ed61cbb99f3c721b4cb72f4f4f","observation_id":"ef5f4d57-53a4-4136-b921-cdf28ba4ccfd","resolution":{"observed_at":"2026-08-15T21:54:57.512717Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.485775Z","title":"XGBoost : A Scalable Tree Boosting System","venue":null,"work_id":"dc4b03e9-7120-4671-afc2-a5e7ddf0e31b","year":2016},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.052600Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:b6c46b77c44540de6d402036c2e5e5da0ebf6ace5bd8fee229ccf7f989abd96d","observation_id":"3501e827-1b4a-4a5d-8c98-335eb6ee57eb","resolution":{"observed_at":"2026-08-15T21:54:57.496311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.469367Z","title":"LightGBM : A Highly Efficient Gradient Boosting Decision Tree","venue":null,"work_id":"582768c3-fd79-4146-917c-78fa268ac7d2","year":2017},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.056203Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:d76bbb491506ac728b79482d01269b2427876ddef68bc2a69ed781535129a852","observation_id":"73ebe581-b601-4684-8504-c7578794222a","resolution":{"observed_at":"2026-08-15T21:54:57.475930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.449688Z","title":"V.; Gulin, A","venue":null,"work_id":"fa8e0313-f69a-4df1-a4f3-e885b628afaa","year":2018},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.061310Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:149b4ec0a40423f9bfd1bfd7b690a14ebb82836ae504cbb4ed7a010218f3930f","observation_id":"f3d4d059-639a-4d26-84ab-54a8873cad3b","resolution":{"observed_at":"2026-08-15T21:54:57.454718Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.436516Z","title":null,"venue":null,"work_id":"9e1135b6-17db-4731-ac05-ca19140d041a","year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.065856Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:69127e2cca0d6ad56a27d628aa409c6b3e2fd5ae26f6f49d35c30dd77431408c","observation_id":"abaa6521-e593-4ee5-8662-b0da52edd078","resolution":{"observed_at":"2026-08-15T21:54:57.440977Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.5281/zenodo.14977096","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.343567Z","title":null,"venue":null,"work_id":"59fbbd56-ea1b-4d4c-a74d-ff69e791d06b","year":2025},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.069419Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:78ab1f3ec57c4cc7f54e078c4f6854993e1f1397c796b36f51481a5bb3f9693e","observation_id":"1b49efbf-3085-4dc8-9145-9018d9e118e1","resolution":{"observed_at":"2026-08-15T21:54:56.348951Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.393605Z","title":"SMILES , a chemical language and information system","venue":null,"work_id":"9c0f06f0-92e1-4a48-b158-baa4782466e8","year":1988},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.073278Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:01e1842e1cba0630e7eb52eb1b12387785a8b5533e4e3c8146eabb30e766dfa6","observation_id":"d5732ee7-49e6-4715-a239-d46e65bebe6a","resolution":{"observed_at":"2026-08-15T21:54:57.427893Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.223834Z","title":null,"venue":null,"work_id":"78155afc-5798-4867-bd08-d48c15108e87","year":2012},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.077102Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f36b64f8775361ce4e9250fdf00ebfe1339cd081cc20e10ee1bd843d9af4121c","observation_id":"64fdad08-c315-4ec4-b4a7-cea562ea2286","resolution":{"observed_at":"2026-08-15T21:54:57.283576Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.199960Z","title":"Self-referencing embedded strings ( SELFIES ): A 100\\","venue":null,"work_id":"d8b6726e-601a-4734-abb1-fed0d2b76eb3","year":null},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.080914Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:5719d1beed1830499b2d0cedc1336d6d617340f45f40f141769b7192820be948","observation_id":"c9dbd38f-03df-48a8-998f-f08c4a564584","resolution":{"observed_at":"2026-08-15T21:54:57.204890Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.181080Z","title":null,"venue":null,"work_id":"0d7298d3-2aed-4e33-95dd-bd4c22355187","year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.084656Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:90f8c59c9280353a9a82ff05a3b94264cdcd3cd79be0caba094e0d6f8d8a9a81","observation_id":"a1be697d-5052-482f-85d9-49226127b14d","resolution":{"observed_at":"2026-08-15T21:54:57.185227Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.164227Z","title":null,"venue":null,"work_id":"5ccde583-f4bd-417b-990f-62bbd154ddf6","year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.089342Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:190af5128a0aec61c1fec2e5ede21a4dad7ab559569c73d5cd3dbd57884dd4b4","observation_id":"1195d789-f58b-4a70-ad66-8946fc393760","resolution":{"observed_at":"2026-08-15T21:54:57.169595Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.148484Z","title":"2019; https://www.python.org/, Version 3.12","venue":null,"work_id":"1cb5321f-d810-4c96-81d3-9a6ebc2b4853","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.093412Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f46def1df9fefd2502afbefe622e82b50668aa4afbc31b2d25bb4883a51ac417","observation_id":"2e2ed3d8-ef18-41f3-ab7e-f0ff52d23d1c","resolution":{"observed_at":"2026-08-15T21:54:57.153362Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.098587Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.098587Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:7ce03d5132bc8707ac6ae60ef7b3bc848a29ea95912a55e0d95caf2d62caa868","observation_id":"743032b0-f2e6-4356-bc27-99ea0c6e9f45","resolution":{"observed_at":"2026-08-15T21:54:56.098587Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.102955Z","title":null,"venue":null,"work_id":null,"year":2011},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.102955Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:68a9305898534754bc346aa1463fd873d4c606ae1489ed1950b01d073a3e0b31","observation_id":"29a7dd3c-b15d-4337-b5dd-d02627487822","resolution":{"observed_at":"2026-08-15T21:54:56.102955Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.10902","last_updated":"2019-07-25T08:55:22Z","snapshot_observed_at":"2026-08-15T20:18:50.969519Z","submitted_at":"2019-07-25T08:55:22Z","title":"Optuna: A Next-generation Hyperparameter Optimization Framework","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.10902","snapshot_observed_at":"2026-08-15T21:54:56.106840Z","title":"Optuna: A Next -generation Hyperparameter Optimization Framework","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.106840Z"},"links":{"cited_paper":"/paper/1907.10902","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f7b82359b44a999acbaa4d6d8b0feb218a1dfd5043717d9132c4381ef4927db5","observation_id":"fbda2130-6b9f-4521-9ea9-0bdfe2f3169d","resolution":{"observed_at":"2026-08-15T21:54:56.106840Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.123331Z","title":"Dask: Parallel Computation with Blocked algorithms and Task Scheduling","venue":null,"work_id":"440fe876-d2bd-407b-be7a-a25bc90f3a19","year":2015},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.111500Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:1395f7a914b373122774bf596346555d8a544e1df859dbbc9774895bc339ec45","observation_id":"040992b9-fe45-403f-aeff-478e7eb4f91d","resolution":{"observed_at":"2026-08-15T21:54:57.130674Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.102810Z","title":"R., Brunno, T","venue":null,"work_id":"afb1e5ac-0e08-42c4-a424-7d49f591a7a6","year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.115642Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:bd301c5555b97954216720fdce39072043d082d9f87d3b7ce02ca386905596f9","observation_id":"d6cfbdb5-1658-4d0f-b24d-eac5cf2d3b46","resolution":{"observed_at":"2026-08-15T21:54:57.107067Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.084923Z","title":"A.; Cheng, T.; Zhang, J.; Gindulyte, A.; Bolton, E","venue":null,"work_id":"a24c4729-e771-4d32-8ee8-ff44e126a380","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.119147Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:e5502ab0fb6b8c565c745b5674437ab0d9e18e93376d193d18399eebbf4b0789","observation_id":"c16f60ee-2c19-44e3-824b-0ce4b68d7e0a","resolution":{"observed_at":"2026-08-15T21:54:57.093110Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.066429Z","title":"https://github.com/mcs07/CIRpy","venue":null,"work_id":"1cfd02e6-d481-4c44-8f47-10b9b08dbb31","year":null},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.123392Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:176eef2f3b7d794f58588f3e79338637826459fcad6fdb0af0cb7d3f2c227262","observation_id":"6277c3ed-7040-42fd-8370-4ae04dfd6aca","resolution":{"observed_at":"2026-08-15T21:54:57.071462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.050363Z","title":"2024; https://github.com/cleanlab/cleanlab, original-date: 2018-05-11T01:55:21Z","venue":null,"work_id":"1119c07c-d74a-43c7-b816-730c7a76b262","year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.127588Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:3042847ef9942a17fd2c936dc126d0ac952a012cdf43eaa0ef035e753371d312","observation_id":"0412dff0-911a-4f66-8158-2b9fc39361d7","resolution":{"observed_at":"2026-08-15T21:54:57.055349Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.024503Z","title":"Detecting Errors in Numerical Data via any Regression Model","venue":null,"work_id":"a9a93816-7f9a-4ec2-ac70-a657686e934e","year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.132388Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f1ff0839df5a5d4ff07ab45aeffb1a8b67ddfb8f786f8e7129321c8a17158ed0","observation_id":"ec2d8438-d49b-4229-b5fc-6df17e3ae1ea","resolution":{"observed_at":"2026-08-15T21:54:57.030359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:57.006776Z","title":"Model-agnostic label quality scoring to detect real-world label errors","venue":null,"work_id":"bcf03c48-2e43-4016-acea-9cb97784d851","year":2022},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.135889Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:ff0eba83062e5e8d4d5945d568c5d610013c773e45b3a77b8efe7b59d256c145","observation_id":"696dce72-fa44-4ec5-86da-7ecfd098d740","resolution":{"observed_at":"2026-08-15T21:54:57.012091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.973805Z","title":null,"venue":null,"work_id":"0ca8480d-c154-411f-80a4-b6c28771fe31","year":2013},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.139485Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:56606192ef1679d20271af01916960d7d505272d34eea92c7f097b31139f0981","observation_id":"e186bb27-860f-4b82-9f42-91661a5cc366","resolution":{"observed_at":"2026-08-15T21:54:56.988022Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.957829Z","title":"M.; Sayle, R","venue":null,"work_id":"ce242a96-a5fc-4091-90e9-e88afe1f785e","year":2016},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.143180Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:75e2e2c5d0aaeb95544c06e4afb313cd5a7ae9945e09a739ffe13b416e1a7427","observation_id":"02f44f01-2c30-4da3-b32c-c47eb2ddb183","resolution":{"observed_at":"2026-08-15T21:54:56.962412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.939594Z","title":null,"venue":null,"work_id":"3396c5d1-76d5-4c87-a03d-bbba21803255","year":2013},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.147006Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:c67533611d1015d63d33ff8d5dda96b081cb27e0d82250856bb2c634beb56209","observation_id":"b0f5e003-38c5-4631-a873-292aa96ec670","resolution":{"observed_at":"2026-08-15T21:54:56.945101Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.917460Z","title":"DeepTox : Toxicity Prediction using Deep Learning","venue":null,"work_id":"f44512ae-eb58-4c34-b8ed-ba32faea549f","year":2016},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.150768Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:e8da2f9942cbe8e41404b8d7a3566c6b32cce3cd06401a4c22ffe409d8ed2845","observation_id":"3ffe4113-219e-47bc-acb4-fe060074a27a","resolution":{"observed_at":"2026-08-15T21:54:56.923691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.903334Z","title":"Profiling Prediction of Kinase Inhibitors : Toward the Virtual Assay","venue":null,"work_id":"922cb413-cb60-40c0-babd-7e30d91baf99","year":2017},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.154245Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:03de69237d8e1b3dfd49a6e311810cd324fa98df20685b1aaa690f3112fcc9dd","observation_id":"66a40893-5567-49ac-b9b6-59f4e6b4a880","resolution":{"observed_at":"2026-08-15T21:54:56.907291Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.886231Z","title":"A.; Fulle, S.; Merget, B","venue":null,"work_id":"399e46ea-72d9-4b70-ab29-f38e24e6fb47","year":2018},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.157578Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:6af4608c1bf20b2da62009a115f0acff0c7fac63b23f3ccce2c4961d2c176577","observation_id":"bde52320-12ff-48f0-8dff-fb13347c4878","resolution":{"observed_at":"2026-08-15T21:54:56.892343Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1301.3781","last_updated":"2013-09-07T00:30:40Z","snapshot_observed_at":"2026-07-06T03:04:11.148340Z","submitted_at":"2013-01-16T18:24:43Z","title":"Efficient Estimation of Word Representations in Vector Space","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1301.3781","snapshot_observed_at":"2026-08-15T21:54:56.161890Z","title":"Efficient Estimation of Word Representations in Vector Space","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.161890Z"},"links":{"cited_paper":"/paper/1301.3781","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f4d792638b4c20ad8f82f935a0307a2c4fe0473effc393d25e86b7711ad36bb8","observation_id":"42857269-0746-4e4d-9151-f172f2a296a4","resolution":{"observed_at":"2026-08-15T21:54:56.161890Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.867593Z","title":"Extended- Connectivity Fingerprints","venue":null,"work_id":"abd54d1a-6605-497a-9ec2-4120e20df846","year":2010},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.165573Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:184a097463087c4bac21bc0063b409e6e39128360032ea4fab8abdb3657edc70","observation_id":"ba78a6b0-bf8a-433d-b9e6-134153bdf717","resolution":{"observed_at":"2026-08-15T21:54:56.875467Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.848645Z","title":"Repurposed drugs and nutraceuticals targeting envelope protein: A possible therapeutic strategy against COVID -19","venue":null,"work_id":"a9f0d403-724f-456e-9a9c-ae4a74fda876","year":2021},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.168922Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:c669aba88a274a995c1990fdb0485418fe68fc263f1c7f359b783373fc335de0","observation_id":"8e7408c4-1958-48f8-b734-490b34ba1e25","resolution":{"observed_at":"2026-08-15T21:54:56.853503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.833614Z","title":"Identifying Structure – Property Relationships through SMILES Syntax Analysis with Self - Attention Mechanism","venue":null,"work_id":"c61cb77b-01e9-47b7-8d62-fc3bb3dde53d","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.172330Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:4d2bcaee5f174a240fcc850602b965479ccd979218b067b84b03d685b2516dea","observation_id":"5e1df902-3e0f-4cc0-82a4-c07299ad4b36","resolution":{"observed_at":"2026-08-15T21:54:56.838380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.821555Z","title":null,"venue":null,"work_id":"dd0ace87-a3ae-48c9-b8ee-81380910fc4d","year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.176583Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:08856cd3a0990cff2f5a561cfa859fcf1140ced6f908f887e0e51d6fc6163840","observation_id":"d85ea893-7357-4460-83e9-5e4478f794a9","resolution":{"observed_at":"2026-08-15T21:54:56.825722Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2105.04906","last_updated":"2022-01-28T12:23:37Z","snapshot_observed_at":"2026-08-21T02:04:15.452338Z","submitted_at":"2021-05-11T09:53:21Z","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2105.04906","snapshot_observed_at":"2026-08-15T21:54:56.180276Z","title":"VICReg : Variance - Invariance - Covariance Regularization for Self - Supervised Learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.180276Z"},"links":{"cited_paper":"/paper/2105.04906","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:4fdb6aed22dd02e67a5ba9d2987ac5acf730ba2e17e375ae1f19f7385538f350","observation_id":"cc770606-49f3-478e-9be4-8f92384036e5","resolution":{"observed_at":"2026-08-15T21:54:56.180276Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.807711Z","title":"Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism","venue":null,"work_id":"9f28422a-2d22-4a09-97a6-f18ba979b066","year":2020},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.184536Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:5c4cd0a8d5f0ba89312b166afafe3972c05fcce23f63650f65ac68ef95f3b79e","observation_id":"549fd189-0758-4839-9d4b-b8daee610ad6","resolution":{"observed_at":"2026-08-15T21:54:56.813544Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.791326Z","title":"Analyzing Learned Molecular Representations for Property Prediction","venue":null,"work_id":"8da05f49-5716-4af7-b6db-02643b39aa39","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.188409Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:caafb190503288776011a764d2a587d9c5991aee40a13562dcb53f0e9f2bf14a","observation_id":"898107eb-143c-4d52-8e35-ed572455b39e","resolution":{"observed_at":"2026-08-15T21:54:56.795927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.777830Z","title":"Correction to Analyzing Learned Molecular Representations for Property Prediction","venue":null,"work_id":"ff42dfdd-590c-40d6-956b-feece5d51e68","year":2019},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.192377Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:0de8d13823d68a60016178cdbb394534489fbaa07a5b788ad3cb07aad6658ad0","observation_id":"fa049f84-3d6a-47f7-bf4a-efcdb2e4033d","resolution":{"observed_at":"2026-08-15T21:54:56.782580Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.02034","last_updated":"2018-03-18T13:50:32Z","snapshot_observed_at":"2026-08-14T20:06:27.036714Z","submitted_at":"2017-12-06T04:29:28Z","title":"SMILES2Vec: An Interpretable General-Purpose Deep Neural Network for Predicting Chemical Properties","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.02034","snapshot_observed_at":"2026-08-15T21:54:56.196609Z","title":"B.; Hodas, N","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.196609Z"},"links":{"cited_paper":"/paper/1712.02034","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:99e2c62ee056e94b7ffdb2dc60735fb76368749442107e80bda77b0eea9a9cbe","observation_id":"d3c1b0c5-9e4f-4d34-bc60-c91547def168","resolution":{"observed_at":"2026-08-15T21:54:56.196609Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2010.09885","last_updated":"2020-10-23T04:22:37Z","snapshot_observed_at":"2026-08-16T19:12:16.763797Z","submitted_at":"2020-10-19T21:41:41Z","title":"ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2010.09885","snapshot_observed_at":"2026-08-15T21:54:56.200935Z","title":"ChemBERTa : Large - Scale Self - Supervised Pretraining for Molecular Property Prediction","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.200935Z"},"links":{"cited_paper":"/paper/2010.09885","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:6bd78b085fb749bf048011e4ef3cafa62c29310269007a8d6c77dafa081043b7","observation_id":"1a8f236a-49ba-4620-ab45-bfc1c7d68809","resolution":{"observed_at":"2026-08-15T21:54:56.200935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.765103Z","title":"Mol- BERT : An Effective Molecular Representation with BERT for Molecular Property Prediction","venue":null,"work_id":"61ca438c-ed49-4249-b046-2243a360531d","year":2021},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.204831Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:b461e3bbb8edc8d83f483e6310fce6421b021da857486560934a2cfb0b3c7e00","observation_id":"f569bd0a-6108-41cf-a29f-c6c86d20816a","resolution":{"observed_at":"2026-08-15T21:54:56.769550Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2106.09553","last_updated":"2022-12-14T14:52:16Z","snapshot_observed_at":"2026-08-16T18:16:22.344871Z","submitted_at":"2021-06-17T14:33:55Z","title":"Large-Scale Chemical Language Representations Capture Molecular Structure and Properties","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.09553","snapshot_observed_at":"2026-08-15T21:54:56.208819Z","title":"Large- Scale Chemical Language Representations Capture Molecular Structure and Properties","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.208819Z"},"links":{"cited_paper":"/paper/2106.09553","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:02864394fb48033d64fd465068f4f29d1a1184835912691047896827ae39bb7c","observation_id":"7fdcdd73-1899-4e06-8312-1d11a87e10f3","resolution":{"observed_at":"2026-08-15T21:54:56.208819Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.747084Z","title":"G.; Jung, G.; Cole, J","venue":null,"work_id":"c7c01ea9-1cd8-422a-a2e7-6a50556b3db6","year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.213730Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:900fb694276466f63678db0cf105f601a7eff9fe76c3fe00fdeac7a6b8f959e8","observation_id":"47903eda-6e89-46dd-8833-b9fe6b0500c7","resolution":{"observed_at":"2026-08-15T21:54:56.751453Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.731589Z","title":"G.; Jung, G.; Cole, J","venue":null,"work_id":"d6c2f25f-9789-4e10-9a08-b3c8414698f1","year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":58,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.218043Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:61efd5a91985b3d331d4ff206b13ebdd2cb7fe13d2c2e9374a8ca96fc82f6017","observation_id":"468104f5-d8aa-40ac-a2fe-fae11bde83ac","resolution":{"observed_at":"2026-08-15T21:54:56.736151Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.714299Z","title":"Functionality pattern matching as an efficient complementary structure/reaction search tool: an open-source approach","venue":null,"work_id":"3d524f2a-fc67-4b9c-8350-b464917f1f3f","year":2010},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":59,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.222441Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:ba8ac1e5ad446a7a1562eb2a2ef228f3b18ff0f4a9c1c7e83631b04e1ffa4d94","observation_id":"f2df66df-635f-459b-8557-7b18ae89cb5a","resolution":{"observed_at":"2026-08-15T21:54:56.720592Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.699461Z","title":"A Density - Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise","venue":null,"work_id":"37648a89-c39d-4142-bf19-fa0e497e7fa3","year":1996},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":60,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.226634Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:49a0bd40ec370cb3ebf37c19265ae14592d4b87099d8746b39d49c1da764035c","observation_id":"b569e8ed-3e62-4ba3-b944-8ce220b14442","resolution":{"observed_at":"2026-08-15T21:54:56.704404Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.678922Z","title":"P.; Xu, X","venue":null,"work_id":"a3846c43-9a13-45c7-9cc1-ae36b0b965d9","year":2017},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":61,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.230079Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:854cd54d656f95e8e6693d314c7345b89d329ca17432d82efd7340a2c65d395b","observation_id":"b145e347-16dc-4e9f-bd05-f8418db7d31a","resolution":{"observed_at":"2026-08-15T21:54:56.686879Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2304.11127","last_updated":"2026-05-31T18:23:52Z","snapshot_observed_at":"2026-08-16T15:38:38.818961Z","submitted_at":"2023-04-21T17:02:38Z","title":"Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.11127","snapshot_observed_at":"2026-08-15T21:54:56.233229Z","title":"Tree- Structured Parzen Estimator : Understanding Its Algorithm Components and Their Roles for Better Empirical Performance","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.233229Z"},"links":{"cited_paper":"/paper/2304.11127","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:a74c590efa15720acefbc319ec1f3a09ee50c56418fbb023265a3a1c68ddfd88","observation_id":"58b8f6c4-fdd7-424b-9551-d18cbfeee1ec","resolution":{"observed_at":"2026-08-15T21:54:56.233229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1206.2944","last_updated":"2012-08-29T06:36:23Z","snapshot_observed_at":"2026-08-18T04:25:34.706968Z","submitted_at":"2012-06-13T21:23:15Z","title":"Practical Bayesian Optimization of Machine Learning Algorithms","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1206.2944","snapshot_observed_at":"2026-08-15T21:54:56.236815Z","title":null,"venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":63,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.236815Z"},"links":{"cited_paper":"/paper/1206.2944","citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:d33830ad0e7e4fa66528e52f340972e8a59dde63a8cdc25f3ef9857cb318a53b","observation_id":"fcfdb427-ecf5-4a14-a54b-2f2732bfd85d","resolution":{"observed_at":"2026-08-15T21:54:56.236815Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.652087Z","title":null,"venue":null,"work_id":"b9682f32-22e4-465f-8caa-32f18e7d8ecc","year":2018},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":64,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.240428Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:f181465c1cf58b99c14368370ee361ce76e1388b131a6ffa2ca7595938683877","observation_id":"c0e093d3-a72d-4476-a08c-b13749b7f8fc","resolution":{"observed_at":"2026-08-15T21:54:56.656078Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.632778Z","title":"V.; Sushko, Y.; Novotarskyi, S.; Patiny, L.; Kondratov, I.; Petrenko, A","venue":null,"work_id":"7f6792a0-629b-46ac-929b-eecc72518792","year":2014},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":65,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.243736Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:6edf72a6a680488c657b10797fc79ccd60c43c8a5593869e2ee4d156c6a0d87b","observation_id":"0e61f9e5-f1f3-4e72-ab00-c333a203e9e2","resolution":{"observed_at":"2026-08-15T21:54:56.641311Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T21:54:56.246937Z","title":"sWE䮨 &HHܳȌ犈? _o_|? G|W |W CK ׃/__ ۿm w s<S] ǥw k 5 ;s Ꮖ ]5y?=n hH ; ) =ۿHGG;)+BL=;4A|O &>","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML","version":1},"reference_index":66,"source":"arxiv_source","source_observed_at":"2026-08-15T21:54:56.246937Z"},"links":{"citing_paper":"/paper/2505.08688"},"observation_digest":"sha256:134fcc219f64abdf8fe555a5bdfd07fc8056c9e21a6e1b958825d764f1576de2","observation_id":"96ebb7c6-857b-4f57-bf44-36f31d1ab60a","resolution":{"observed_at":"2026-08-15T21:54:56.246937Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2505.08688","last_updated":"2025-05-13T15:46:26Z","latest_version":1,"primary_category":"physics.chem-ph","snapshot_observed_at":"2026-08-20T11:03:11.435442Z","submitted_at":"2025-05-13T15:46:26Z","title":"A Machine Learning Pipeline for Molecular Property Prediction using ChemXploreML"},"reference_resolution":{"displayed":66,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":24,"verified_exact":2,"verified_fuzzy":40},"total_outbound_references":66},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-23T06:30:58.430688+00:00","source":"crossref"},{"observed_at":"2026-08-23T06:30:53.778098+00:00","source":"retraction_watch"}],"thesis":"As of 23 August 2026, this Paper Citation Record lists 66 of 66 outbound references and 0 inbound Pith citation observations for arXiv:2505.08688."}