{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UOBS5J2UZE3ZW7YWDXAGKKWDJ2","short_pith_number":"pith:UOBS5J2U","schema_version":"1.0","canonical_sha256":"a3832ea754c9379b7f161dc0652ac34ea56ed9cf96123d73dd9399010e195203","source":{"kind":"arxiv","id":"2504.17247","version":3},"attestation_state":"computed","paper":{"title":"OmegAMP: Targeted AMP Discovery via Biologically Informed Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Cesar de la Fuente-Nunez, Diogo Soares, Ewa Szczurek, Fabian Theis, Johanna Sommer, Leon Hetzel, Marcelo Der Torossian Torres, Paulina Szymczak, Stephan G\\\"unnemann","submitted_at":"2025-04-24T04:53:04Z","abstract_excerpt":"Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates. To address these challenges, we introduce OmegAMP, a framework designed for reliable AMP generation with increased controllability. Its diffusion-based generative model leverages a novel conditioning mechanism to achieve fine-grained control over desired physicochemical properties and to direct generation towards specific activity profiles, including species-specific effectiv"},"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":"2504.17247","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-04-24T04:53:04Z","cross_cats_sorted":["cs.AI","q-bio.BM"],"title_canon_sha256":"a2df05da0c0232cf75f48fc61139e146d864df52d0d2ffbcc919d24a94cc0f14","abstract_canon_sha256":"a135d7d1dfec1c5a3cbddebed43ad153292eba0512946d33abc773c432c680bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-25T01:18:34.805417Z","signature_b64":"orE4Ko/atAi4Ba2nJp4PGd4bigHJfWBz0QaVzwO8ypCcEjoxK1do5DebRIFYIn53orbIHSPPi6DH2BANryGjAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a3832ea754c9379b7f161dc0652ac34ea56ed9cf96123d73dd9399010e195203","last_reissued_at":"2026-06-25T01:18:34.804904Z","signature_status":"signed_v1","first_computed_at":"2026-06-25T01:18:34.804904Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"OmegAMP: Targeted AMP Discovery via Biologically Informed Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","q-bio.BM"],"primary_cat":"cs.LG","authors_text":"Cesar de la Fuente-Nunez, Diogo Soares, Ewa Szczurek, Fabian Theis, Johanna Sommer, Leon Hetzel, Marcelo Der Torossian Torres, Paulina Szymczak, Stephan G\\\"unnemann","submitted_at":"2025-04-24T04:53:04Z","abstract_excerpt":"Deep learning-based antimicrobial peptide (AMP) discovery faces critical challenges such as limited controllability, lack of representations that efficiently model antimicrobial properties, and low experimental hit rates. To address these challenges, we introduce OmegAMP, a framework designed for reliable AMP generation with increased controllability. Its diffusion-based generative model leverages a novel conditioning mechanism to achieve fine-grained control over desired physicochemical properties and to direct generation towards specific activity profiles, including species-specific effectiv"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17247","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/2504.17247/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":"2504.17247","created_at":"2026-06-25T01:18:34.804961+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17247v3","created_at":"2026-06-25T01:18:34.804961+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17247","created_at":"2026-06-25T01:18:34.804961+00:00"},{"alias_kind":"pith_short_12","alias_value":"UOBS5J2UZE3Z","created_at":"2026-06-25T01:18:34.804961+00:00"},{"alias_kind":"pith_short_16","alias_value":"UOBS5J2UZE3ZW7YW","created_at":"2026-06-25T01:18:34.804961+00:00"},{"alias_kind":"pith_short_8","alias_value":"UOBS5J2U","created_at":"2026-06-25T01:18:34.804961+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2","json":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2.json","graph_json":"https://pith.science/api/pith-number/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/graph.json","events_json":"https://pith.science/api/pith-number/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/events.json","paper":"https://pith.science/paper/UOBS5J2U"},"agent_actions":{"view_html":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2","download_json":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2.json","view_paper":"https://pith.science/paper/UOBS5J2U","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17247&json=true","fetch_graph":"https://pith.science/api/pith-number/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/graph.json","fetch_events":"https://pith.science/api/pith-number/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/action/storage_attestation","attest_author":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/action/author_attestation","sign_citation":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/action/citation_signature","submit_replication":"https://pith.science/pith/UOBS5J2UZE3ZW7YWDXAGKKWDJ2/action/replication_record"}},"created_at":"2026-06-25T01:18:34.804961+00:00","updated_at":"2026-06-25T01:18:34.804961+00:00"}