{"as_of":"2026-08-20T11:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:badf435717dd297cc78a7777b522409ec1bb81b6bc61a300d14db65c34d6f1e9","coverage":[{"denominator":77,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":77,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-16T05:20:56.915651Z","state":"measured"},{"denominator":77,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":77,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+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/2504.21092/citation-record","integrity":"/paper/2504.21092/integrity","json":"/paper/2504.21092/citation-record.json","paper":"/paper/2504.21092"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:58.129866Z","title":"Uncovering protein function: from classification to complexes","venue":null,"work_id":"b51ede47-b7dc-421d-8233-8dacd2e2c9e4","year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.598565Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:c3b923bc8b6f33c5bce20e233d55f3e86feaefcc751d39d9b7096c3a9de9c64c","observation_id":"68307e5a-9d84-4c26-aa61-2db6ad16b450","resolution":{"observed_at":"2026-08-16T05:20:58.134134Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:58.110120Z","title":"Sensing the shape of functional proteins with topology","venue":null,"work_id":"87462440-e511-466d-b620-fbdd1bb1bea2","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.604256Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:2eab7799b84efe363b517af8ed5468c57035d472f7a73f00e3c12519bc9482b1","observation_id":"fa3bc8f9-b3a2-4d3d-9921-6f854bf51cde","resolution":{"observed_at":"2026-08-16T05:20:58.116092Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:58.092331Z","title":"The coming of age of de novo protein design","venue":null,"work_id":"deb956a5-c4dd-4e82-9d65-cfad1957d6a3","year":2016},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.608806Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:9560260b4536a4b2c7405f148e9dd4afdf17fbace26e210735334099b009ce34","observation_id":"4d42af63-1569-4d19-9321-d9984cf4dd91","resolution":{"observed_at":"2026-08-16T05:20:58.097910Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:56.613512Z","title":"Recent advances in de novo protein design: principles, methods, and applications","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.613512Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:f784f8a481a08618eba24a36602845e8a1426cb9f18cc63a8bf021c59e609c35","observation_id":"4da100f8-54ea-4e42-9581-fed1adb82c17","resolution":{"observed_at":"2026-08-16T05:20:56.613512Z","resolver_source":null,"status":"malformed_identifier"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2408.17241","last_updated":"2024-09-13T19:01:39Z","snapshot_observed_at":"2026-08-16T16:16:29.449074Z","submitted_at":"2024-08-30T12:32:40Z","title":"Leveraging Deep Generative Model For Computational Protein Design And Optimization","version":2},"cited_work":{"arxiv_id":"2408.17241","doi":"10.48550/arxiv.2408.17241","metadata_source":"pith","pith_arxiv_id":"2408.17241","snapshot_observed_at":"2026-08-16T12:16:17.039197Z","title":"Leveraging Deep Generative Model For Computational Protein Design And Optimization","venue":"q-bio.BM","work_id":"fc9089ab-2e9f-462d-8064-9f9421aa7f59","year":2024},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.617517Z"},"links":{"cited_paper":"/paper/2408.17241","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:fef1d9ce59138f4b4538d3b09241ff91366438deabf9f19a46b12c656a7b0b84","observation_id":"d94fb0d4-4127-474e-b01f-0da0d8e3829e","resolution":{"observed_at":"2026-08-16T05:20:57.074219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1093/bib/bbae289","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:57.052397Z","title":"A comprehensive review and comparison of existing com- putational methods for protein function prediction","venue":null,"work_id":"7b1d317e-7fd8-4fa6-b1d3-6668e510543b","year":2024},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.622009Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:fdc7a0fb8e2b0b36017bcafe5957d70cf1005fb3301186324f25e74cca62d5fc","observation_id":"e3df1814-a10a-4608-b6e0-62b19653b743","resolution":{"observed_at":"2026-08-16T05:20:57.056726Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:58.073392Z","title":"Generative adversarial nets","venue":null,"work_id":"70f07e8d-c1db-4cd6-b6d1-464c8536d328","year":2014},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.626889Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:fdc1592e5c7db96fad5d4fc483497436bde2de65200cc5b8d9b7a8f6ae5a380c","observation_id":"02254cd5-049d-43f1-8bca-ef10e8d08f22","resolution":{"observed_at":"2026-08-16T05:20:58.078538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6114","last_updated":"2022-12-10T21:04:00Z","snapshot_observed_at":"2026-08-14T23:50:45.029465Z","submitted_at":"2013-12-20T20:58:10Z","title":"Auto-Encoding Variational Bayes","version":11},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6114","snapshot_observed_at":"2026-08-16T05:20:56.630101Z","title":"Auto-encoding variational bayes","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.630101Z"},"links":{"cited_paper":"/paper/1312.6114","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:e469e1795aadae3b387a8509dc9c80b638b6322cd70afb73306452b9e08c381d","observation_id":"97607dec-a3aa-4e39-8fe8-5a4ca14bf9bf","resolution":{"observed_at":"2026-08-16T05:20:56.630101Z","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":"2026-08-16T05:20:58.048784Z","title":"Variational inference with normalizing flows","venue":null,"work_id":"dd2ee6fe-4597-4469-8d4b-23984353bf12","year":2015},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.633474Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:478ae3c2bf237f2ceb3e0742492a48cb1c6055ef9caa40b079e25794a6b4ee34","observation_id":"25f66e80-b42e-497c-9238-bdde9ada085a","resolution":{"observed_at":"2026-08-16T05:20:58.055758Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:58.032792Z","title":"Generative modeling for protein structures","venue":null,"work_id":"7040a9a5-8602-47ca-bccd-7ed3eabec3ed","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.636574Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:f7b9d9e05633d63384deb09b626743035212a681bc4d09f4822681c4924fd791","observation_id":"7c37d085-b551-4488-b29d-ba94e3d1ca75","resolution":{"observed_at":"2026-08-16T05:20:58.037992Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:58.014910Z","title":"ProteinVAE: Variational autoencoder for trans- lational protein design","venue":null,"work_id":"fe7d4241-858a-47fc-9270-b13a39fbb732","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.640193Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:78a61ee0961b65c34ca30b3951f771ae2f9a6b314a578c33f2fe1aed4c93fc27","observation_id":"4d219cfd-0cc1-4212-a809-5755e83ccfca","resolution":{"observed_at":"2026-08-16T05:20:58.022013Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.994745Z","title":"ProtTrans: Toward understanding the language of life through self-supervised learning","venue":null,"work_id":"8bda8f3d-6c70-4197-b8f9-2b28f0397806","year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.643803Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:6dec6ff71d902f1d4add3445cbce3e7bf1018bb10d1fc18d9a835d5c631fa8a8","observation_id":"91e7c84f-92c9-4cab-98ea-56289a562d42","resolution":{"observed_at":"2026-08-16T05:20:58.000133Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.979124Z","title":"Prot-VAE: Protein transformer variational au- toencoder for functional protein design","venue":null,"work_id":"0449cfd6-1c70-4c12-9084-bba70022f2b0","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.647396Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:8a6d6ba776d043cc22249141185f1755e62bc9823cc2da7c79a883c09df8a33f","observation_id":"22f79f4a-0127-467b-9782-e6d472a74251","resolution":{"observed_at":"2026-08-16T05:20:57.984876Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1038/s42256-021-00310-5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:57.036072Z","title":"Expanding functional protein sequence spaces using generative adversarial networks","venue":null,"work_id":"7dcbed19-ce32-48d4-980a-233e38a78835","year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.651002Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:fe4e0c643d75c8970a0bf3b4847954cdbab9d1e468b4c375e9e40502a65a0906","observation_id":"8b3220fd-529e-4a28-969b-11f6571002d1","resolution":{"observed_at":"2026-08-16T05:20:57.041312Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1016/j.sbi.2021.11.008","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:57.019336Z","title":"Deep generative modeling for protein design","venue":null,"work_id":"958c284c-a35a-446e-877b-5c0613b8a052","year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.654745Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:deaf9fb52eb9e889caec716a9839f567a0826ebc07908c84d159d5f159c90dcc","observation_id":"eb0dc814-1879-41ca-a972-d09d035dcce5","resolution":{"observed_at":"2026-08-16T05:20:57.025576Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.954621Z","title":"Computational protein design with deep learning neural networks","venue":null,"work_id":"8f5cf4fb-7f5f-4c1e-94db-1f213c7097d7","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.658730Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:c3b47a297b553360eedcb4a5f460a0a0206fe5433315e300f63dac11e020203e","observation_id":"1f7fb4f0-859c-4082-b04f-3fe27b5f3b07","resolution":{"observed_at":"2026-08-16T05:20:57.960068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.933351Z","title":"DenseCPD: improving the accuracy of neural-network-based computational protein sequence design with DenseNet","venue":null,"work_id":"a946cfb9-5e13-4701-9ff8-6b5f86086b09","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.662341Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:b4b7a832dbd80f3a80735ba9dc76b5c285a35384d55badc04012474ee77e1ca3","observation_id":"d2079f9f-d85b-4ed1-9245-af07497374ab","resolution":{"observed_at":"2026-08-16T05:20:57.938424Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.921093Z","title":"De novo protein design by deep network hallucination","venue":null,"work_id":"34ff5e19-aca2-4ad7-bd6a-637a21fd0d9d","year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.666115Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:783acb7fd762b79df76b90c0ad8638cbad01d8af87b995126f6bffbd97c4f854","observation_id":"9af03f05-00a4-4ef8-85e6-e8011b67f2cd","resolution":{"observed_at":"2026-08-16T05:20:57.925523Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.903860Z","title":"Robust deep learning-based protein sequence design using Pro- teinMPNN","venue":null,"work_id":"f49777dc-207b-45a0-a2a5-d6cccbee7f96","year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.670082Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:3539ed3ff9fa6255846719c8245f8a543b64b59d49b590d629169c839267d99f","observation_id":"1c83352c-9268-493d-9153-eafbcdb63000","resolution":{"observed_at":"2026-08-16T05:20:57.912233Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.880678Z","title":"ProtGPT2 is a deep unsupervised language model for protein design","venue":null,"work_id":"0dee10b8-eaad-4658-bc88-090eee04058f","year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.674095Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:217686ed57ab6fb292d5d3c6f4b57e140cb0331146114dbd235249fc3fd56be0","observation_id":"12bb2856-227f-4551-9277-981a2415348e","resolution":{"observed_at":"2026-08-16T05:20:57.887090Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.860244Z","title":"Score-based gen- erative modeling through stochastic differential equations","venue":null,"work_id":"6ff88ead-24fd-4869-8463-c32ba8cff5c0","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.678030Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:c3014b2668e5864ae4f7cb9d721a569485e511e01212da9af691dba0c6bcd1be","observation_id":"ebe39b1e-4551-4af3-b0e2-650d50eb0b71","resolution":{"observed_at":"2026-08-16T05:20:57.865654Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.847833Z","title":"Denoising diffusion probabilistic models","venue":null,"work_id":"e8d8087d-ff0e-4cef-bdf8-df14f22ed82d","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.681670Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:be017021a49b7065ebb41c35d6624014526b334d9f6d05f60975bf92a239c854","observation_id":"ec8e1fec-8c5a-421d-8dd4-13d7632864c9","resolution":{"observed_at":"2026-08-16T05:20:57.851959Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.835119Z","title":"Generative modeling by estimating gradients of the data distribu- tion","venue":null,"work_id":"9abb3c5f-2f67-40f9-8fdd-5e6e7b37b805","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.685119Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:8e615f40a0c50f15e749c6141e036e1ad3d7f323384f5706deb0b22a598a3c0e","observation_id":"772a845c-33b8-4b91-9448-ee32cb23d46f","resolution":{"observed_at":"2026-08-16T05:20:57.839623Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.820442Z","title":"Score-based generative models with L´ evy processes","venue":null,"work_id":"e66a0480-464c-4a13-a6be-1207a233a713","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.688854Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:e541debffdab30447cdd50003a464feda8286abb9c4aff659f1ce9aabc167c96","observation_id":"f6821627-3e51-4ecd-9bc2-fbb6e0d7e1b3","resolution":{"observed_at":"2026-08-16T05:20:57.825207Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.804208Z","title":"Annealed fractional L´ evy–It¯ o diffusion models for protein generation","venue":null,"work_id":"970d2c81-1150-4637-840e-a6c3fdd65e38","year":2024},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.692778Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:3b4ba67f6f574f40dc121c02c4d8429e320c0262ab5a814f5b12614283fc23a7","observation_id":"0cca9576-1f92-4ccb-86ad-fb44baa5321f","resolution":{"observed_at":"2026-08-16T05:20:57.810583Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2310.17638","last_updated":"2024-10-31T14:35:05Z","snapshot_observed_at":"2026-08-20T06:27:01.338382Z","submitted_at":"2023-10-26T17:53:24Z","title":"Generative Fractional Diffusion Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.17638","snapshot_observed_at":"2026-08-16T05:20:56.696251Z","title":"Generative fractional diffusion models","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.696251Z"},"links":{"cited_paper":"/paper/2310.17638","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:4117a363be80bd74ab86c8dd0601eaac0cef7bea460110b0deb010ad86f1b7b3","observation_id":"58ea9b61-68df-4a99-9ec9-182c80d5337d","resolution":{"observed_at":"2026-08-16T05:20:56.696251Z","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":"10.1016/j.spa.2018.04.010","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:57.004847Z","title":"Affine representations of fractional processes with appli- cations in mathematical finance","venue":null,"work_id":"d4a73b12-1f5c-4789-9aa3-60b946c29f72","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.700461Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:0c3140fbc520a5a779d14c6db724a2b96c0f4af73bd7e756b8d0549eccab6112","observation_id":"a701c0c3-3ce6-4e42-97c7-3d49472fdb66","resolution":{"observed_at":"2026-08-16T05:20:57.009350Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.791317Z","title":"Variational inference for SDEs driven by fractional noise","venue":null,"work_id":"69a1671e-ede1-4f1f-b01e-6e9aa021f4ef","year":null},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.704772Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:00412a743769dd35a4e0085326e341167b76a07bafe3b96e09a132f4a8e109de","observation_id":"5bd85433-4327-49f4-9e5d-4a474fec1425","resolution":{"observed_at":"2026-08-16T05:20:57.795850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.767821Z","title":"Improved denoising diffusion probabilistic models","venue":null,"work_id":"21a928fc-271c-4012-9d26-605dcf825b2f","year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.713302Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:4a18670f044cbfb327856a39e2f99b2c6001c7eda78d20ee7867ea2a79cc711a","observation_id":"f7385940-8d2b-4fbb-b523-f231e589c46a","resolution":{"observed_at":"2026-08-16T05:20:57.771653Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.756053Z","title":"Reverse-time diffusion equation models","venue":null,"work_id":"c4fd87ae-dc1a-44a9-804d-8ae9a8abe02a","year":1982},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.717298Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:5699314fc14f237feea7f0c36a37281ef999111906b416750a77a1b0a4c88814","observation_id":"e6854f36-3e37-48be-be46-db6dc19df1c4","resolution":{"observed_at":"2026-08-16T05:20:57.760305Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.744609Z","title":"Numerical solution of stochastic differential equations","venue":null,"work_id":"e4cdbba2-fc65-454c-b63e-3302d6d830f4","year":1992},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.721116Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:1c9df77d90b70eac97ffec8492f5e2396b4a6316eba94854260e1e6f36ab6792","observation_id":"2006d1ad-ffaf-4f60-918e-0a7d9ce42a38","resolution":{"observed_at":"2026-08-16T05:20:57.748528Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.730637Z","title":"Neural ordinary differential equations","venue":null,"work_id":"8d9acc79-bd3f-4196-a255-cfb39e858aac","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.725132Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:30567cc025d453201c4759127ef7b066cd22f2a970063830e9de5d25fe0f34d8","observation_id":"20b0544e-073c-42c0-aa2b-fe283aba8eec","resolution":{"observed_at":"2026-08-16T05:20:57.735336Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:56.729896Z","title":"Basic local alignment search tool","venue":null,"work_id":null,"year":1990},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.729896Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:9a9f41126d3542f1a95386d503cf8602e9c1f326afe75aeaa74dd44c6fc5bdcc","observation_id":"d06be5cb-4a90-42e0-aecf-a732f512cc06","resolution":{"observed_at":"2026-08-16T05:20:56.729896Z","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":"2026-08-16T05:20:57.718963Z","title":"DeepRED: automated protein function prediction with multi-task feed-forward deep neural networks","venue":null,"work_id":"7031df3c-b4b7-48a7-91af-6101458d2b4b","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.734046Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:a0b5001e2e9b23cfa3edd0178150cc738b8e582d2f8e57de44dddf1e3ceb0e98","observation_id":"c10b507c-ceb8-4df6-990a-960c0cc121f0","resolution":{"observed_at":"2026-08-16T05:20:57.722983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.707709Z","title":"DeepGOPlus: improved protein function predic- tion from sequence","venue":null,"work_id":"be60f8fa-3256-4c5f-bbff-13913a96efef","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.738209Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:77a69e4aa18a240bd930955eb37c4140a02a54a75ef88c487ce3149dd3d49cb4","observation_id":"00bf258c-d74c-4a16-a0bb-f84f4acdd41b","resolution":{"observed_at":"2026-08-16T05:20:57.711451Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:56.743559Z","title":"Structure-based protein function prediction using graph convolutional networks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.743559Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:ae4ae31baa931e4455abb43bb151cbb007d1975fec81e1e2125138667c8c39ae","observation_id":"7cb77705-e99f-4dfe-80a4-177ff1c776ef","resolution":{"observed_at":"2026-08-16T05:20:56.743559Z","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":"2026-08-16T05:20:56.748190Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.748190Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:07c3454cba28e8ac3d17b1ecc3003d0d8841c30165249bb137f7bc6d7716bf28","observation_id":"c7a1151f-5e88-4257-80c1-03b30839c038","resolution":{"observed_at":"2026-08-16T05:20:56.748190Z","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":"10.1093/bioinformatics/btad410","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:56.962519Z","title":"Hierarchical graph transformer with contrastive learning for protein function prediction","venue":null,"work_id":"ac8395a5-4b7d-4b36-88ad-dadde551f840","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.752171Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:277191dd0c5e2dd177bb16dffe8e655c385a4787ffd81d1f716b00a2ab9d7325","observation_id":"7ec18654-0026-4e6d-9ce3-bba57ef2638f","resolution":{"observed_at":"2026-08-16T05:20:56.966891Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.695537Z","title":"GeneMANIA: a real-time multiple as- sociation network integration algorithm for predicting gene function","venue":null,"work_id":"21f2b032-09c0-4f76-b74b-510a6f6cf5fd","year":2008},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.756167Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:f4660416835cf3a3e8636f2939062bffec0f11e073634468ed817abbb9f75358","observation_id":"f6f680c4-790e-4b7c-ae63-e2aa13a20a21","resolution":{"observed_at":"2026-08-16T05:20:57.700148Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.683861Z","title":"DeepNF: deep network fusion for protein function prediction","venue":null,"work_id":"ce7d90f8-7ca2-4df7-87fd-ba5740da5a08","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.760141Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:95ef2f6fbb0f4c46bfc120608d4e9707c3c16df470f8e5af9d13719c97624af0","observation_id":"eefea9d5-3cab-4d01-8918-70f1fa595391","resolution":{"observed_at":"2026-08-16T05:20:57.687680Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.670392Z","title":"The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens","venue":null,"work_id":"7e7d425b-a839-49ca-9599-6541cd696b21","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.763657Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:dd29cb79cf943c126d74d38cffd29e0ea70c1eda678c8d7e7d659f16edbc0169","observation_id":"fc031113-5f0a-47a2-82e0-781fd0963a04","resolution":{"observed_at":"2026-08-16T05:20:57.675409Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.656009Z","title":"A comprehensive review and comparison of ex- isting computational methods for protein function prediction","venue":null,"work_id":"07c9666c-492f-4612-9b56-7789212f1b04","year":2024},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.767433Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:35dd6edf0c1139a1cd6c1561f77599ea3b7fb52a25f77ede9c5335cca3956cd0","observation_id":"50caefd9-378a-419b-b602-ddbe3a34e131","resolution":{"observed_at":"2026-08-16T05:20:57.661639Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.641868Z","title":"Generative models for pro- tein sequence modeling: recent advances and future directions","venue":null,"work_id":"5e6fe2ab-aa37-4355-ab7a-7d7669058c9b","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.771393Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:351b74e59348ae80667b3091b8db46fb49ac7bbd8cd273d60caedb0784ea9801","observation_id":"7c7e1dc5-b8b6-4204-adde-16956ebfe9ac","resolution":{"observed_at":"2026-08-16T05:20:57.647116Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.625695Z","title":"Scaffolding protein functional sites using deep learning","venue":null,"work_id":"cb6f3074-3310-4626-8716-6ba5059a884c","year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.775577Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:bf96d6aa935f1f4243661d3d001478935e912cc9aeaf992210da92766ed1d9b7","observation_id":"f476dcad-aea3-4b30-a22f-5cf0993d2a1b","resolution":{"observed_at":"2026-08-16T05:20:57.631867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.609626Z","title":"Score-based generative modeling for de novo protein design","venue":null,"work_id":"d0103b80-0eeb-4917-82d1-44dad10ec3ad","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.779674Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:fe308c86a4b5fe51297217cc0cf95f06b6f457b235c89027d833829d59bff233","observation_id":"d54da6ef-af74-4280-b5b6-3c3e9332111f","resolution":{"observed_at":"2026-08-16T05:20:57.616222Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.591805Z","title":"The Protein Data Bank: a computer-based archival file for macromolecular structures","venue":null,"work_id":"f05d3d17-5dbd-44ad-8297-c77c0e671d3e","year":1978},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.783524Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:a88335c1b35e0c68e74727ae941752ea4c8a8a4db3409f8cee2b28833177facb","observation_id":"b2a0cd0f-ca8c-49de-912b-d17aaa16fcf2","resolution":{"observed_at":"2026-08-16T05:20:57.597252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.576756Z","title":"Generative modeling for protein structures","venue":null,"work_id":"3018d337-a1d3-429e-9aba-66596c0150ee","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.788069Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:9eb45b58f0902da31fd0074d9e1c23e4dbe2c5b019384e2e7741918941cdb4e3","observation_id":"82bd4cc3-e835-48e7-a4a8-05674b528904","resolution":{"observed_at":"2026-08-16T05:20:57.582252Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.561151Z","title":"Sampling realistic protein conformations using local structural bias","venue":null,"work_id":"89c2f075-852d-4281-b9eb-88a22ad424b5","year":2006},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.793014Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:8b2fb490fa9ba10bb3440e195bea95d44fdf2fb3d092afbb3ff0b2884368b2ca","observation_id":"c178bdb9-756b-47db-a40f-b4cb2f13941f","resolution":{"observed_at":"2026-08-16T05:20:57.566725Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.544647Z","title":"A generative, probabilistic model of local protein structure","venue":null,"work_id":"28362588-6c59-4763-9976-caffd526ac94","year":2008},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.797113Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:15a40800a516b1a98933ea04cf960782a02ed7ad74bea7c4259167ed0111193a","observation_id":"335c1a50-68e8-4cd0-99bd-55ffe6bbdea7","resolution":{"observed_at":"2026-08-16T05:20:57.550766Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.525024Z","title":"Learning a probabilistic latent space of object shapes via 3D generative-adversarial modeling","venue":null,"work_id":"c4a10e9c-ace2-4c03-8d3d-53e4bb111981","year":2016},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.801631Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:5a0194025fd898855c19c7c7e85d5457e320a1e9027f1b9c51fec378aaeaa58c","observation_id":"e7aff876-b6fa-4916-bef3-2a563c90d2fe","resolution":{"observed_at":"2026-08-16T05:20:57.531213Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.511225Z","title":"Protein structure prediction using Rosetta","venue":null,"work_id":"f3bfb916-4ca2-43ce-a6f1-65a04d53912f","year":2004},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.806088Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:f1ff6384dfb10ca4137d2943f20257f42ce6d44bd87bc57007afa11bb3808831","observation_id":"5c180004-8d9b-47ce-b8f4-2e64171e9da8","resolution":{"observed_at":"2026-08-16T05:20:57.516226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.496404Z","title":"Distributed optimization and statistical learning via the alternating direction method of multipliers","venue":null,"work_id":"aff7db05-7516-417c-92b9-e5dd69082237","year":2011},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.809863Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:eb2c6e319cb69e53228b967307c4b3972f54ab3ebe7136e847a6308fd1dee28a","observation_id":"94104c62-d2ae-4e7c-9510-2d59d6e6b66d","resolution":{"observed_at":"2026-08-16T05:20:57.502068Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.480290Z","title":"Fully differentiable full-atom protein backbone generation","venue":null,"work_id":"a3013b73-d4e3-4154-897b-92d486be6010","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.813555Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:c758f6d35a05bd023b0542cffd6ba050b6a6d6e236ee452d57d63e2e36a8150d","observation_id":"5dc17f42-a782-48a1-ab93-ed550d1b00f4","resolution":{"observed_at":"2026-08-16T05:20:57.487407Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1142/s2010194515600022","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-16T05:20:56.946584Z","title":"Fractional Brownian motion in a nutshell","venue":null,"work_id":"a96a11dc-dd18-4566-b03a-a0445b5eebdd","year":2015},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.817346Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:2298627275f3e2dd920086c87eca3bba651cac86b6fb5fb855e25a317daeb7ab","observation_id":"bceb837d-fce0-4708-95f6-cf3382f74249","resolution":{"observed_at":"2026-08-16T05:20:56.953686Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.466460Z","title":"Estimation of non-normalized statistical models by score matching","venue":null,"work_id":"d5785d15-4f84-4773-aa92-b086643ffc89","year":2005},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.821058Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:7af532b1d12d22ebb0845b6ecd2e9f0960e042fa5b3ff0628f6bf72c25fe6b64","observation_id":"cc299ee1-cf31-4781-9280-fe10b01fd85e","resolution":{"observed_at":"2026-08-16T05:20:57.471939Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.451387Z","title":"Estimating the Hessian by backpropagating cur- vature","venue":null,"work_id":"5fd12cbf-d2df-4bed-85b8-76c72918e150","year":2012},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.824757Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:dc8f1a0371ccf0e18cd61f07a44b7f76d43f495cdf7653cd4fc2a9760007a2b4","observation_id":"19cef6e7-a005-43fe-8cda-f60a7cd4d241","resolution":{"observed_at":"2026-08-16T05:20:57.455704Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.438727Z","title":"Sliced score matching: A scalable approach to density and score estimation","venue":null,"work_id":"f4a32559-9839-4646-acc5-3cfc1da279a4","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.829037Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:282ac4927526a50b8e1d5b495266b7f3c88e2e93a61a3c6f4507cf7513683b24","observation_id":"c0097bad-63af-4a42-9168-8a4ca10c240a","resolution":{"observed_at":"2026-08-16T05:20:57.443342Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.426393Z","title":"A connection between score matching and denoising autoencoders","venue":null,"work_id":"03fd7327-8096-4a44-8237-4eaab687519c","year":2011},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.832818Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:6c5f7547a23e668e4e2776e7c14216090b2bdd0edee209f45e940698955c7d1c","observation_id":"986fa476-51df-4d7e-8f72-53ab4a16d65b","resolution":{"observed_at":"2026-08-16T05:20:57.430847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.414894Z","title":"Generative modeling by estimating gradients of the data distribu- tion","venue":null,"work_id":"358f18ed-8a3c-442e-8bfb-a03e8ad926c3","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.837644Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:b2057412570e3d092020e9119d04eef38cfc17941b973f14b9ff349af4f7c5bb","observation_id":"67bf8b79-a16b-4727-9d63-95f9ae404840","resolution":{"observed_at":"2026-08-16T05:20:57.419216Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.403266Z","title":"Improved techniques for training score-based generative models","venue":null,"work_id":"5025231a-f8f8-4df3-bc05-e9eeaaa44d96","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.841654Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:ad7d4e7b8d38e4525fce50b91527e77a606df54345e6bf070ea652b898ea9b7a","observation_id":"9f92714e-f270-4a87-8026-a9c59e680665","resolution":{"observed_at":"2026-08-16T05:20:57.407570Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.391757Z","title":"Denoising diffusion implicit models","venue":null,"work_id":"11f6ebc1-5cf2-4c32-a37a-dd3ef7cedc38","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.845718Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:23061a89287b5c61559b8ecc99ce160a2c35ca2e7452b208d7f133a5c56e4a6d","observation_id":"3d17880a-60bf-4fb3-affe-19fa236a8f54","resolution":{"observed_at":"2026-08-16T05:20:57.396033Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.379642Z","title":"Numerical solution of stochastic differential equations","venue":null,"work_id":"17d0d730-563f-4cfe-9087-ae8d517f89fc","year":null},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.850079Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:6ebe4ddc4b747f750181e3b15fa3c85a1abc317cb03ec9dfa0095c953719f680","observation_id":"3a4b90fe-f1cc-4ef5-a462-7ca9e2da52bb","resolution":{"observed_at":"2026-08-16T05:20:57.383518Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.356537Z","title":"Approximate integration of stochastic differential equations","venue":null,"work_id":"c2b33a74-39b9-4121-8d2c-5548d179924c","year":1975},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.858568Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:5d7e6bca505b7c6ff10f0be641ec776c5fa9e445b8362547eff334eaa84c6132","observation_id":"84d18e92-9789-434c-8c4b-97f2fd9c3acd","resolution":{"observed_at":"2026-08-16T05:20:57.360397Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.367955Z","title":null,"venue":null,"work_id":"eac06ca0-0f02-48f9-9e21-6082e0d6ac2d","year":2013},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.854519Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:4c25facd75911ac04de66652abe1ee5eba48d4ec9d9f960e573f4360974f8468","observation_id":"91d25415-4e17-469b-be89-106fc83a4e39","resolution":{"observed_at":"2026-08-16T05:20:57.371865Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2305.11798","last_updated":"2023-05-19T16:33:05Z","snapshot_observed_at":"2026-08-16T16:15:11.863034Z","submitted_at":"2023-05-19T16:33:05Z","title":"The probability flow ODE is provably fast","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.11798","snapshot_observed_at":"2026-08-16T05:20:56.867751Z","title":"The probability flow ODE is provably fast","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.867751Z"},"links":{"cited_paper":"/paper/2305.11798","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:b43d4eb0bee549094604f4a1102023c1f2b5f7f66c74e592cea5d8be2b2df313","observation_id":"04654977-a975-4b5c-b2dd-7eb4d2cf9731","resolution":{"observed_at":"2026-08-16T05:20:56.867751Z","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":"2026-08-16T05:20:57.344147Z","title":"Numerical continuation methods: an introduction","venue":null,"work_id":"61ccde8f-9d30-4fd8-9a5b-9c809696c465","year":2012},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.863552Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:876ed3a9f04328c25b5e9cc00ceb31b69660ddf7fe7d46c9a70a2d55fa8c8f5b","observation_id":"50a9fa00-9a14-4412-a547-9a7a6b20bc53","resolution":{"observed_at":"2026-08-16T05:20:57.348318Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2204.13902","last_updated":"2023-02-25T20:30:35Z","snapshot_observed_at":"2026-08-16T17:03:46.549576Z","submitted_at":"2022-04-29T06:32:38Z","title":"Fast Sampling of Diffusion Models with Exponential Integrator","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2204.13902","snapshot_observed_at":"2026-08-16T05:20:56.878415Z","title":"Fast sampling of diffusion models with exponential integrator","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.878415Z"},"links":{"cited_paper":"/paper/2204.13902","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:013c4b453a314635cbeb3e67b758f859018aca4ccd339981d7c909efcc8ac42b","observation_id":"76fbfc4b-d336-49ac-944a-38cd95424248","resolution":{"observed_at":"2026-08-16T05:20:56.878415Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2206.00927","last_updated":"2022-10-13T12:04:36Z","snapshot_observed_at":"2026-08-16T16:55:48.381197Z","submitted_at":"2022-06-02T08:43:16Z","title":"DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2206.00927","snapshot_observed_at":"2026-08-16T05:20:56.872585Z","title":"DPM-solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.872585Z"},"links":{"cited_paper":"/paper/2206.00927","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:2e87bb4f366d303989a4a639b6229c05a8d00ef10a39041ae355d06f0229d97b","observation_id":"749c1533-d289-44ac-a5c4-526efe0da94d","resolution":{"observed_at":"2026-08-16T05:20:56.872585Z","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":"2026-08-16T05:20:57.318776Z","title":"U-Net: convolutional networks for biomedical im- age segmentation","venue":null,"work_id":"e3ff8775-47c9-4b16-90d8-2a11c534178b","year":2015},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.890327Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:965b33f72bbeacc0b3338526c5b6c7162c2eb68c08e262f7771d18e255a5f10b","observation_id":"f94b36bf-d2fa-4213-9973-7970d821e3b3","resolution":{"observed_at":"2026-08-16T05:20:57.323226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.331910Z","title":"Variational diffusion models","venue":null,"work_id":"55066184-426d-4761-8dd2-83fec4faf731","year":2021},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.885687Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:97058ad1e51ff718e3ac417c749f463992b19ab79f32459bb277aa754e835299","observation_id":"c6f01347-d989-4221-aad9-e48100c1c312","resolution":{"observed_at":"2026-08-16T05:20:57.336359Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.291680Z","title":"Improved precision and recall metric for assessing generative models","venue":null,"work_id":"f2c53f1c-5f03-464a-b6e0-db9bf98c0f26","year":2019},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.898856Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:6fda6d8624a5f56e32fcc552ef1d7b2b86d846cb0fca830c4a8ae6c13c9d5af7","observation_id":"f1629177-13d1-4217-b316-ecf2c85ed75a","resolution":{"observed_at":"2026-08-16T05:20:57.296844Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.306449Z","title":"Assessing generative models via precision and recall","venue":null,"work_id":"1a70d34d-ef27-4221-864f-413b8637178b","year":2018},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.894549Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:6dbd0b20dec59d4d8a0ace2abda05733eaff2ed2500a70d9e80b8fed25f740cb","observation_id":"617faded-cb4e-4cd5-a265-886315de56b5","resolution":{"observed_at":"2026-08-16T05:20:57.310689Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:56.907152Z","title":"Is noise conditioning necessary for denoising generative models? arXiv preprint","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.907152Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:4df8db15660ca577bac3068a517486aafb19d1ddcaaf0cf73b1d3e176e2735b9","observation_id":"4be01c5e-2c3b-4b7c-886a-11c0481d37ca","resolution":{"observed_at":"2026-08-16T05:20:56.907152Z","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":"2026-08-16T05:20:57.277360Z","title":"Reliable fidelity and diversity metrics for generative models","venue":null,"work_id":"b3bb871b-31f2-459e-abc8-1a2f3b871d11","year":2020},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.902719Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:c0071f319ce2408f5c94c7d916f60de6e70b67606f4281cda5613568beb6ce5f","observation_id":"fe4dcf93-64ef-4e19-9145-1fa3da0133da","resolution":{"observed_at":"2026-08-16T05:20:57.281965Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+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":"2026-08-16T05:20:57.263953Z","title":"Score nor- malization for a faster diffusion exponential integrator sampler","venue":null,"work_id":"5b2adffc-8cbb-444a-b21d-d9d0b8bc11ff","year":2023},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.915651Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:23a8a9cecbc4c33262998ac11fadfe8486da2858caa3ac843da4f69c100d8881","observation_id":"56ace3fc-6777-41d0-8e2e-42ec3236e39c","resolution":{"observed_at":"2026-08-16T05:20:57.268881Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.01095","last_updated":"2025-05-19T07:56:56Z","snapshot_observed_at":"2026-08-18T13:49:48.200480Z","submitted_at":"2022-11-02T13:14:30Z","title":"DPM-Solver++: Fast Solver for Guided Sampling of Diffusion Probabilistic Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.01095","snapshot_observed_at":"2026-08-16T05:20:56.911297Z","title":"DPM-Solver++: fast solver for guided sampling of diffusion probabilistic models","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.911297Z"},"links":{"cited_paper":"/paper/2211.01095","citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:d591c97b0f38b214be3bd1a63ac62678e3bfde9218bb8c32ecd404cf3bdb614f","observation_id":"7146f517-bb86-4363-8068-90a3cc16c306","resolution":{"observed_at":"2026-08-16T05:20:56.911297Z","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":"2026-08-16T05:20:57.778792Z","title":null,"venue":null,"work_id":"ca7cfcba-52d9-4f05-86fd-d45fea43d9fe","year":null},"citing_paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation","version":1},"reference_index":2024,"source":"pdf_text","source_observed_at":"2026-08-16T05:20:56.709084Z"},"links":{"citing_paper":"/paper/2504.21092"},"observation_digest":"sha256:0e3e263add749b4b107ed23f2297cfaebdcf4c46f078e662bbeaf88492cbe6fd","observation_id":"955ed3ba-fb35-4d41-834c-26ac9a797c42","resolution":{"observed_at":"2026-08-16T05:20:57.782658Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2504.21092","last_updated":"2025-04-29T18:03:51Z","latest_version":1,"primary_category":"q-bio.QM","snapshot_observed_at":"2026-08-16T16:15:46.316671Z","submitted_at":"2025-04-29T18:03:51Z","title":"ProT-GFDM: A Generative Fractional Diffusion Model for Protein Generation"},"reference_resolution":{"displayed":77,"state_counts":{"malformed_identifier":3,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":12,"verified_exact":7,"verified_fuzzy":55},"total_outbound_references":77},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 77 of 77 outbound references and 0 inbound Pith citation observations for arXiv:2504.21092."}