{"id":"W2040921700","doi":"10.1016/s0003-2697(02)00608-5","title":"Identification of metabotropic glutamate receptor antagonists using an automated high-throughput screening system","year":2003,"lang":"en","type":"article","venue":"Analytical Biochemistry","topic":"Neuroscience and Neuropharmacology Research","field":"Neuroscience","cited_by":39,"is_retracted":false,"has_abstract":false,"ca_institutions":"Merck Canada Inc. (Canada)","funders":"","keywords":"Metabotropic glutamate receptor; Metabotropic glutamate receptor 5; Metabotropic glutamate receptor 1; Metabotropic glutamate receptor 4; Metabotropic glutamate receptor 2; Metabotropic glutamate receptor 7; Metabotropic glutamate receptor 6; Metabotropic glutamate receptor 3; High-throughput screening; Metabotropic receptor; Glutamate receptor; Agonist; Metabotropic glutamate receptor 8; Pharmacology; Chemistry; Receptor; Neuroscience; Medicine; Biology; Biochemistry","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":[0.0003853297,0.0001895364,0.0002911461,0.00009806974,0.0002630643,0.00009037437,0.0004570559,0.0001185272,0.00006907741],"category_scores_gemma":[0.0006491415,0.0001796398,0.00009005842,0.0007898426,0.0005304298,0.0002497091,0.00008038586,0.0002419394,0.00005088386],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006120968,"about_ca_system_score_gemma":0.0001259354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009989311,"about_ca_topic_score_gemma":6.879925e-8,"domain_scores_codex":[0.9973658,0.000344883,0.0005219906,0.000768723,0.0005240521,0.0004745428],"domain_scores_gemma":[0.9988316,0.0001415702,0.0002076057,0.0004906919,0.0001031248,0.0002254044],"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":[0.00003074164,0.0001535545,0.0001728082,0.00009833342,0.000007056181,0.00005021296,0.00000690309,0.0001148615,0.9983126,0.0009489161,0.00006835653,0.00003568802],"study_design_scores_gemma":[0.000280397,0.00005659298,0.00005805315,0.00002105212,0.00004199695,0.00005397247,0.00003373328,0.06633626,0.9328806,0.0000128535,0.00007528501,0.0001491491],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9984893,0.00002210758,0.000595715,0.00008311689,0.0003059081,0.000139908,0.00004094782,0.0002347123,0.00008824096],"genre_scores_gemma":[0.9993284,0.00001573081,0.000243482,0.0001115263,0.00005219487,0.000005904925,0.000002719027,0.00002214915,0.0002178681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06622139,"threshold_uncertainty_score":0.7325497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05869137781071139,"score_gpt":0.3728199714046453,"score_spread":0.314128593593934,"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."}}