{"id":"W2566166953","doi":"10.1021/acs.analchem.6b03625","title":"The “PepSAVI-MS” Pipeline for Natural Product Bioactive Peptide Discovery","year":2016,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Biochemical and Structural Characterization","field":"Biochemistry, Genetics and Molecular Biology","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Higher Education Commission, Pakistan; Ministry of Science and Technology, Pakistan; United States Agency for International Development; National Institute of General Medical Sciences; U.S. Department of State","keywords":"Chemistry; Natural product; Antimicrobial; Peptide; Antifungal; Drug discovery; Computational biology; Antibiotics; Antibacterial activity; Identification (biology); Biochemistry; Bacteria; Microbiology; Biology; Botany","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009066951,0.0001738412,0.0001278297,0.000005270807,0.000126986,0.00005157345,0.0002320289,0.0001161087,0.00001361953],"category_scores_gemma":[0.0005815749,0.00008780228,0.0001639565,0.00006229021,0.000234579,0.00001119619,0.00009162522,0.00008632655,0.000004121339],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002416595,"about_ca_system_score_gemma":0.00004431703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001807012,"about_ca_topic_score_gemma":0.00000187422,"domain_scores_codex":[0.9989341,0.00001224184,0.0002124493,0.0004162503,0.0001233811,0.000301501],"domain_scores_gemma":[0.9993094,0.00006531109,0.00007212103,0.0003313917,0.0001377791,0.00008404254],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002170497,0.00001564986,0.000231734,0.00001829963,0.00004289862,4.640133e-7,0.000001861951,1.535539e-7,0.9906157,0.0001128193,0.004274009,0.004469402],"study_design_scores_gemma":[0.0003532039,0.00002478295,0.0007414694,0.00001343832,0.00002833423,0.00001016381,0.000009438083,0.0001143804,0.9466565,0.0003092175,0.05155114,0.0001879324],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894434,0.0003998895,0.003183418,0.005235356,0.0001709431,0.0002174896,0.0001073564,0.00002382054,0.001218285],"genre_scores_gemma":[0.9686995,0.00008911508,0.00007739227,0.0001638351,0.001026555,0.00002582622,0.0002365208,0.00001672864,0.02966456],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04727713,"threshold_uncertainty_score":0.3580473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006638980382419782,"score_gpt":0.2391754693789107,"score_spread":0.232536488996491,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}