{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:LUT4XJI2P7HDXE24QPGRA7I6L3","short_pith_number":"pith:LUT4XJI2","schema_version":"1.0","canonical_sha256":"5d27cba51a7fce3b935c83cd107d1e5ef501373f1663f9628867c86ab9f4dcb3","source":{"kind":"arxiv","id":"2310.03842","version":3},"attestation_state":"computed","paper":{"title":"PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.BM","authors_text":"Christina Peng, Connor Monticello, Divya Srijay, Hector C. Aguilar, Kseniia Kholina, Lauren Hong, Lin Zhao, Madeleine Dumas, Matthew P. DeLisa, Mayumi Shaepers-Cheu, Pranam Chatterjee, Pranay Vure, Ray Truant, Rio Watson, Rishab Pulugurta, Sarah Pertsemlidis, Shrey Goel, Sophia Vincoff, Tianlai Chen, Tian Zi Wang","submitted_at":"2023-10-05T18:59:51Z","abstract_excerpt":"Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access \"undruggable\" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2310.03842","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"q-bio.BM","submitted_at":"2023-10-05T18:59:51Z","cross_cats_sorted":[],"title_canon_sha256":"61d2ab867bec7c97a3054b69faf8e8b425d906723829575b020953f43718baa5","abstract_canon_sha256":"18b27dd2cf1beaa5a93ca6203202ca9c82c278d44427e8896e34939b29edcdbb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:54:04.200228Z","signature_b64":"7JM2nPJEQy6q1LC0c6iFf3a8fh8TPXyq7zCHkX3IEIuV1ogRm0H8wdYfS/DQGZzPwJ/5IZ5Jrn1mimD/qh3CAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d27cba51a7fce3b935c83cd107d1e5ef501373f1663f9628867c86ab9f4dcb3","last_reissued_at":"2026-07-05T08:54:04.199752Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:54:04.199752Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PepMLM: Target Sequence-Conditioned Generation of Therapeutic Peptide Binders via Span Masked Language Modeling","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"q-bio.BM","authors_text":"Christina Peng, Connor Monticello, Divya Srijay, Hector C. Aguilar, Kseniia Kholina, Lauren Hong, Lin Zhao, Madeleine Dumas, Matthew P. DeLisa, Mayumi Shaepers-Cheu, Pranam Chatterjee, Pranay Vure, Ray Truant, Rio Watson, Rishab Pulugurta, Sarah Pertsemlidis, Shrey Goel, Sophia Vincoff, Tianlai Chen, Tian Zi Wang","submitted_at":"2023-10-05T18:59:51Z","abstract_excerpt":"Target proteins that lack accessible binding pockets and conformational stability have posed increasing challenges for drug development. Induced proximity strategies, such as PROTACs and molecular glues, have thus gained attention as pharmacological alternatives, but still require small molecule docking at binding pockets for targeted protein degradation. The computational design of protein-based binders presents unique opportunities to access \"undruggable\" targets, but have often relied on stable 3D structures or structure-influenced latent spaces for effective binder generation. In this work"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.03842","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2310.03842/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2310.03842","created_at":"2026-07-05T08:54:04.199808+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.03842v3","created_at":"2026-07-05T08:54:04.199808+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.03842","created_at":"2026-07-05T08:54:04.199808+00:00"},{"alias_kind":"pith_short_12","alias_value":"LUT4XJI2P7HD","created_at":"2026-07-05T08:54:04.199808+00:00"},{"alias_kind":"pith_short_16","alias_value":"LUT4XJI2P7HDXE24","created_at":"2026-07-05T08:54:04.199808+00:00"},{"alias_kind":"pith_short_8","alias_value":"LUT4XJI2","created_at":"2026-07-05T08:54:04.199808+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.02264","citing_title":"NLP4Neuro: Sequence-to-sequence learning for neural population decoding","ref_index":10,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3","json":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3.json","graph_json":"https://pith.science/api/pith-number/LUT4XJI2P7HDXE24QPGRA7I6L3/graph.json","events_json":"https://pith.science/api/pith-number/LUT4XJI2P7HDXE24QPGRA7I6L3/events.json","paper":"https://pith.science/paper/LUT4XJI2"},"agent_actions":{"view_html":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3","download_json":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3.json","view_paper":"https://pith.science/paper/LUT4XJI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.03842&json=true","fetch_graph":"https://pith.science/api/pith-number/LUT4XJI2P7HDXE24QPGRA7I6L3/graph.json","fetch_events":"https://pith.science/api/pith-number/LUT4XJI2P7HDXE24QPGRA7I6L3/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3/action/storage_attestation","attest_author":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3/action/author_attestation","sign_citation":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3/action/citation_signature","submit_replication":"https://pith.science/pith/LUT4XJI2P7HDXE24QPGRA7I6L3/action/replication_record"}},"created_at":"2026-07-05T08:54:04.199808+00:00","updated_at":"2026-07-05T08:54:04.199808+00:00"}