{"id":"W2604613721","doi":"10.1021/acs.jmedchem.6b01848","title":"Use of Molecular Modeling to Design Selective NTS2 Neurotensin Analogues","year":2017,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Neuropeptides and Animal Physiology","field":"Neuroscience","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"Canadian Institutes of Health Research; Université de Montpellier; Canada Research Chairs; Agence Nationale de la Recherche","keywords":"Neurotensin; Chemistry; Residue (chemistry); Neurotensin receptor; Molecular model; Stereochemistry; Receptor; Amino acid residue; Mutant; Molecular dynamics; Biochemistry; Peptide sequence; Neuropeptide; Gene","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":[0.0002018706,0.001014828,0.0008711407,0.0002187452,0.0003658829,0.0005825865,0.0006096814,0.0005692342,0.001913402],"category_scores_gemma":[0.0003336027,0.0003307198,0.0005371105,0.0002287855,0.0002111034,0.000418841,0.000359214,0.0006760367,0.000365889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005630742,"about_ca_system_score_gemma":0.0008020706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002900423,"about_ca_topic_score_gemma":0.003059633,"domain_scores_codex":[0.9999021,0.00002234549,0.000004984394,0.00001522776,0.00002729224,0.00002793746],"domain_scores_gemma":[0.9999185,0.00002434004,0.00001763186,0.000005692363,0.0000190939,0.00001483625],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004892171,0.0002954167,0.001224457,0.0003281799,0.0001253516,0.0005993879,0.00009076452,0.9116315,0.06347681,0.00932232,0.0007603143,0.01165626],"study_design_scores_gemma":[0.0001702726,0.0003994137,0.0002373021,0.00001728285,0.00005328967,0.00005873173,0.00004919511,0.9833531,0.01137609,0.001057274,0.003207468,0.00002054559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8668412,0.002144717,0.1057753,0.0005668344,0.0001390817,0.0003301657,0.0008194115,0.0006860963,0.02269709],"genre_scores_gemma":[0.9317744,0.001958693,0.06233127,0.0001447287,0.00001974603,0.0004185064,0.0007293913,0.0001104843,0.002512734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002900423,"threshold_uncertainty_score":0.006400943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1432433268074371,"score_gpt":0.3171868182544048,"score_spread":0.1739434914469677,"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."}}