{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005284151,0.0005775116,0.0008222336,0.0006194707,0.0002555521,0.0009298211,0.0009714002,0.0008119228,0.002318003],"category_scores_gemma":[0.0006746356,0.0003424397,0.000407008,0.0005195155,0.0004137963,0.0005756236,0.0008105418,0.0008270032,0.001040929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006465741,"about_ca_system_score_gemma":0.0005363252,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000515998,"about_ca_topic_score_gemma":0.001406272,"domain_scores_codex":[0.9994907,0.00006776152,0.00001952042,0.00007269285,0.0002656247,0.00008370479],"domain_scores_gemma":[0.99976,0.00006267887,0.00005819042,0.00003866426,0.00005169618,0.00002882547],"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":[0.0003453432,0.00007676837,0.0003003443,0.0002375393,0.00003519992,0.0001143208,0.00003679139,0.0007568941,0.9645585,0.001341143,0.001653157,0.03054414],"study_design_scores_gemma":[0.00005436965,0.0003463927,0.0005816539,0.00001556247,0.0000268058,0.000175575,0.00002928333,0.006280476,0.9867069,0.0002907142,0.005471384,0.00002080832],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7343447,0.01370173,0.2157885,0.001874228,0.0003212457,0.0006605434,0.00370453,0.003767931,0.02583642],"genre_scores_gemma":[0.8616256,0.007405883,0.1134811,0.0007510065,0.000068281,0.0003871065,0.002126395,0.0001848509,0.01396968],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002318003,"threshold_uncertainty_score":0.007754445,"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."}}