{"id":"W3086904036","doi":"10.1021/acs.jmedchem.0c01376","title":"Optimized Opioid-Neurotensin Multitarget Peptides: From Design to Structure–Activity Relationship Studies","year":2020,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Neuropeptides and Animal Physiology","field":"Neuroscience","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Research Chairs; Austrian Science Fund; Universität Innsbruck; Fonds de Recherche du Québec - Santé; Fonds Wetenschappelijk Onderzoek","keywords":"Neurotensin; Chemistry; Tetrapeptide; Pharmacophore; Neurotensin receptor; Agonist; Nociceptin receptor; Stereochemistry; Peptide; Opioid; Selectivity; Receptor; Partial agonist; Structure–activity relationship; Dermorphin; Opioid peptide; Pharmacology; Biochemistry; Neuropeptide; In vitro","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003636935,0.0004598121,0.0004479342,0.0002482232,0.0001511954,0.0003716041,0.0004411459,0.0002376371,0.001781777],"category_scores_gemma":[0.0002342254,0.0002106337,0.000222236,0.0002874805,0.0001335216,0.0002893994,0.0002413984,0.0005267368,0.00038863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004567492,"about_ca_system_score_gemma":0.000351062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000613248,"about_ca_topic_score_gemma":0.002516682,"domain_scores_codex":[0.9998907,0.00001846707,0.000008950758,0.00002041807,0.00002834369,0.00003304983],"domain_scores_gemma":[0.9999212,0.000009821281,0.00002269702,0.000004278449,0.00002141229,0.00002059309],"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":[0.001106038,0.0004258796,0.0003560664,0.0003442042,0.0000618488,0.0002984506,0.00005335418,0.003585818,0.9687029,0.0006738676,0.0004523943,0.02393903],"study_design_scores_gemma":[0.0009253423,0.01164534,0.004303713,0.00006574939,0.0003122533,0.001402312,0.0001008484,0.005694527,0.9523044,0.0003717093,0.02281314,0.00006064157],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9496515,0.01558533,0.02520531,0.0003167564,0.00009747627,0.0008730929,0.001641044,0.0001696803,0.006459831],"genre_scores_gemma":[0.9647849,0.008337624,0.0205718,0.0001556626,0.00002168714,0.000180445,0.001403047,0.00002538159,0.00451932],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001781777,"threshold_uncertainty_score":0.005960703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.149217867772127,"score_gpt":0.3288373008782497,"score_spread":0.1796194331061227,"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."}}