{"id":"W2506776793","doi":"10.1016/j.ejmech.2016.07.078","title":"Bridging computational modeling with amino acid replacements to investigate GHS-R1a-peptidomimetic recognition","year":2016,"lang":"en","type":"article","venue":"European Journal of Medicinal Chemistry","topic":"Regulation of Appetite and Obesity","field":"Neuroscience","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University; Lawson Health Research Institute","funders":"","keywords":"Peptidomimetic; Chemistry; Docking (animal); Ghrelin; G protein-coupled receptor; Molecular mechanics; Homology modeling; Stereochemistry; Receptor; Agonist; Ligand (biochemistry); Molecular model; Binding site; Molecular dynamics; Biophysics; Biochemistry; Computational chemistry; Peptide; Biology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005872152,0.001084692,0.001384367,0.0003153984,0.0005904777,0.0009124138,0.00118244,0.0009928368,0.003848002],"category_scores_gemma":[0.001660178,0.0003248141,0.0006445116,0.0004448124,0.0004188076,0.0006695451,0.0004917085,0.001298895,0.0004544097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006526918,"about_ca_system_score_gemma":0.00126118,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005637972,"about_ca_topic_score_gemma":0.00513914,"domain_scores_codex":[0.9997376,0.0001014723,0.00001148335,0.00003413231,0.00006194736,0.00005337799],"domain_scores_gemma":[0.9993511,0.0004665022,0.00004763462,0.00003633942,0.00005764724,0.00004069904],"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":[0.0005821973,0.0002371112,0.001692659,0.000277942,0.0001382546,0.0005067428,0.00009314037,0.967579,0.008451035,0.01047705,0.0009457103,0.009019205],"study_design_scores_gemma":[0.00007795503,0.0001178188,0.0001169823,0.00001161777,0.00003663382,0.00002942052,0.00003727116,0.9955124,0.001728562,0.001805594,0.0005181559,0.000007657599],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9358298,0.00107558,0.04352045,0.0007061733,0.0001429411,0.0001198001,0.0006870655,0.0006567455,0.01726147],"genre_scores_gemma":[0.9823354,0.0003897461,0.01564693,0.0001451542,0.00002436074,0.00009392944,0.000436526,0.00006903568,0.0008590064],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005637972,"threshold_uncertainty_score":0.01287287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03815599548495353,"score_gpt":0.2439566798306961,"score_spread":0.2058006843457426,"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."}}