{"id":"W3129064277","doi":"10.1021/acs.jmedchem.0c01726","title":"Design, Structural Optimization, and Characterization of the First Selective Macrocyclic Neurotensin Receptor Type 2 Non-opioid Analgesic","year":2021,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Neuropeptides and Animal Physiology","field":"Neuroscience","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research; Canada Foundation for Innovation","keywords":"Neurotensin; Chemistry; Neurotensin receptor; In vivo; Receptor; Opioid; Chemical synthesis; Stereochemistry; Pharmacology; Combinatorial chemistry; In vitro; Neuropeptide; Biochemistry; Medicine","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":[0.0001063256,0.0003537929,0.0002506203,0.0001369235,0.0001435023,0.0002232662,0.0002938605,0.0002046801,0.001338913],"category_scores_gemma":[0.0001012891,0.00008655972,0.0001618387,0.0001567327,0.0001193355,0.0001695402,0.0001496167,0.0003170777,0.0004392134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002384101,"about_ca_system_score_gemma":0.0002815218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007129452,"about_ca_topic_score_gemma":0.002883563,"domain_scores_codex":[0.9999444,0.000006049796,0.000003432272,0.00001162112,0.00001428786,0.00002010544],"domain_scores_gemma":[0.9999636,0.000002444049,0.00001199959,0.000003876355,0.000007327301,0.00001072329],"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":[0.0003972965,0.0002075929,0.0003825886,0.0002170221,0.00002711354,0.0003658437,0.00004889919,0.002418101,0.9720743,0.0007587817,0.0005242259,0.0225783],"study_design_scores_gemma":[0.0001853808,0.005573925,0.004147496,0.00002589546,0.00007073024,0.0009854281,0.00006119908,0.003313566,0.9548926,0.0001681796,0.03053293,0.00004265422],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9724218,0.004263222,0.01456694,0.0001770868,0.00008007431,0.000417499,0.0009996288,0.0001256726,0.006948126],"genre_scores_gemma":[0.977213,0.004213654,0.01134449,0.0001331845,0.00002344573,0.0000989477,0.001276624,0.00003049064,0.005666323],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001338913,"threshold_uncertainty_score":0.004479051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01320239192492808,"score_gpt":0.2293872279381508,"score_spread":0.2161848360132227,"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."}}