{"id":"W2990727390","doi":"10.1177/2472555219887142","title":"Image-Based Marker-Free Screening of GABAA Agonists, Antagonists, and Modulators","year":2019,"lang":"en","type":"article","venue":"SLAS DISCOVERY","topic":"Digital Holography and Microscopy","field":"Physics and Astronomy","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"National Center of Competence in Research Chemical Biology; Université de Lausanne; Centre Hospitalier Universitaire Vaudois; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"GABAA receptor; Ionotropic effect; Muscimol; Partial agonist; GABAA-rho receptor; Agonist; Anxiolytic; Virtual screening; Pharmacology; Allosteric modulator; Ion channel; Drug discovery; High-throughput screening; Receptor; Biology; Neuroscience; Bioinformatics; Biochemistry; Glutamate receptor","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":[0.00008266664,0.0001761275,0.0002442129,0.00007494381,0.00005375409,0.0001434472,0.0001927497,0.00003641033,0.0001059326],"category_scores_gemma":[0.000003090765,0.0001641978,0.0001640279,0.000138057,0.0001787959,0.0005544479,0.0001140063,0.00009905711,0.00001418447],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005373552,"about_ca_system_score_gemma":0.00004596933,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009572732,"about_ca_topic_score_gemma":0.000001619319,"domain_scores_codex":[0.9991226,0.00002103671,0.0002043313,0.0002822658,0.000120931,0.0002488481],"domain_scores_gemma":[0.9993453,0.00003904335,0.0001146066,0.0004147147,0.00002773521,0.00005859408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000224016,0.0002553077,0.9307762,0.0001390048,0.0001724758,0.000003724973,0.00005529769,0.00003678464,0.02709856,0.03157829,0.002412328,0.007248031],"study_design_scores_gemma":[0.01141565,0.001035057,0.5714118,0.0008350391,0.0002738903,0.000007576401,0.002109471,0.002427355,0.3447617,0.04127639,0.02198806,0.002457975],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699239,0.0001292718,0.008375619,0.00004626053,0.000161085,0.0001813352,0.0004377729,0.00002187937,0.02072294],"genre_scores_gemma":[0.9954839,0.000002676052,0.003472364,0.00003766278,0.00004588837,0.000005835614,0.00009283066,0.0000219304,0.000836898],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3593643,"threshold_uncertainty_score":0.6695792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003852343122928666,"score_gpt":0.2134675139174834,"score_spread":0.2096151707945547,"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."}}