{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LLY7XPYTO2KZ7IONJXRBYNDZEN","short_pith_number":"pith:LLY7XPYT","schema_version":"1.0","canonical_sha256":"5af1fbbf1376959fa1cd4de21c34792374c240ea405fca0b70c4357dc100fccd","source":{"kind":"arxiv","id":"1810.07743","version":3},"attestation_state":"computed","paper":{"title":"PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Aleksandra Mojsilovic, Cicero dos Santos, Inkit Padhi, Kahini Wadhawan, Matthew Riemer, Oscar Chang, Payel Das, Tom Sercu, Vijil Chenthamarakshan","submitted_at":"2018-10-17T19:19:36Z","abstract_excerpt":"Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences. Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences. The model further learns a disentangled antimicrobial attribute space by using the feedback from a jointly trained AMP classifier that uses limited labeled in"},"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":"1810.07743","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2018-10-17T19:19:36Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"d0ec321f854640043d2f4ff1ca97f26e9e1f0cdd4841c3e6da9afab287945475","abstract_canon_sha256":"c393560afec560eaca75eba64cd0292ebd5c0917d1a61be421b654a9319f9787"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:47.219804Z","signature_b64":"C+PzIQ1Hb/7VTTUqa8KqBh9Yzf03jrgnBn2K8LttxdCNdEmHFebNssz38RxScx+rFS+49Z4b337ZzfKioNHcCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5af1fbbf1376959fa1cd4de21c34792374c240ea405fca0b70c4357dc100fccd","last_reissued_at":"2026-05-18T00:00:47.219326Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:47.219326Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PepCVAE: Semi-Supervised Targeted Design of Antimicrobial Peptide Sequences","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Aleksandra Mojsilovic, Cicero dos Santos, Inkit Padhi, Kahini Wadhawan, Matthew Riemer, Oscar Chang, Payel Das, Tom Sercu, Vijil Chenthamarakshan","submitted_at":"2018-10-17T19:19:36Z","abstract_excerpt":"Given the emerging global threat of antimicrobial resistance, new methods for next-generation antimicrobial design are urgently needed. We report a peptide generation framework PepCVAE, based on a semi-supervised variational autoencoder (VAE) model, for designing novel antimicrobial peptide (AMP) sequences. Our model learns a rich latent space of the biological peptide context by taking advantage of abundant, unlabeled peptide sequences. The model further learns a disentangled antimicrobial attribute space by using the feedback from a jointly trained AMP classifier that uses limited labeled in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.07743","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":""},"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":"1810.07743","created_at":"2026-05-18T00:00:47.219409+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.07743v3","created_at":"2026-05-18T00:00:47.219409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.07743","created_at":"2026-05-18T00:00:47.219409+00:00"},{"alias_kind":"pith_short_12","alias_value":"LLY7XPYTO2KZ","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LLY7XPYTO2KZ7ION","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LLY7XPYT","created_at":"2026-05-18T12:32:37.024351+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.02060","citing_title":"Morphology-Aware Peptide Discovery via Masked Conditional Generative Modeling","ref_index":13,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN","json":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN.json","graph_json":"https://pith.science/api/pith-number/LLY7XPYTO2KZ7IONJXRBYNDZEN/graph.json","events_json":"https://pith.science/api/pith-number/LLY7XPYTO2KZ7IONJXRBYNDZEN/events.json","paper":"https://pith.science/paper/LLY7XPYT"},"agent_actions":{"view_html":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN","download_json":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN.json","view_paper":"https://pith.science/paper/LLY7XPYT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.07743&json=true","fetch_graph":"https://pith.science/api/pith-number/LLY7XPYTO2KZ7IONJXRBYNDZEN/graph.json","fetch_events":"https://pith.science/api/pith-number/LLY7XPYTO2KZ7IONJXRBYNDZEN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN/action/storage_attestation","attest_author":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN/action/author_attestation","sign_citation":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN/action/citation_signature","submit_replication":"https://pith.science/pith/LLY7XPYTO2KZ7IONJXRBYNDZEN/action/replication_record"}},"created_at":"2026-05-18T00:00:47.219409+00:00","updated_at":"2026-05-18T00:00:47.219409+00:00"}