{"id":"W2073477117","doi":"10.1016/j.jmgm.2014.10.010","title":"Identifying potential selective fluorescent probes for cancer-associated protein carbonic anhydrase IX using a computational approach","year":2014,"lang":"en","type":"article","venue":"Journal of Molecular Graphics and Modelling","topic":"Enzyme function and inhibition","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Lakehead University; Thunder Bay Regional Research Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Bank of Canada; Royal Bank of Canada; Lakehead University","keywords":"Virtual screening; Fluorescence; Chemistry; Computational biology; Carbonic anhydrase; Fluorophore; In silico; Combinatorial chemistry; Biochemistry; Biology; Enzyme; Drug discovery","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004369529,0.0001318539,0.0001737799,0.0001273736,0.0001538803,0.00005488262,0.00006332039,0.0001266776,4.596972e-7],"category_scores_gemma":[0.0000363974,0.0001276482,0.0001677514,0.000104792,0.00004222092,0.0000126727,0.00002398381,0.0001532839,2.231753e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000256034,"about_ca_system_score_gemma":0.0001015162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002157942,"about_ca_topic_score_gemma":0.000004057002,"domain_scores_codex":[0.9990317,0.00008047993,0.0003046017,0.0002019781,0.0002062073,0.0001750303],"domain_scores_gemma":[0.9990647,0.00000618227,0.000310524,0.00006415934,0.0004718996,0.00008255622],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001167936,0.00009520861,0.00006022085,0.00004526269,0.0001300914,0.000001543759,0.00004747837,0.4568902,0.5415483,0.0008997351,0.00002411876,0.0001411371],"study_design_scores_gemma":[0.001206232,0.0003474792,0.00001889703,0.0001151702,0.0001102175,0.00003644289,0.00003629698,0.8293808,0.1647286,0.003765299,0.00008217994,0.0001724255],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4963377,0.0005592254,0.5028816,0.00002426433,0.00004696528,0.000136539,0.000004363759,0.000002382639,0.00000686965],"genre_scores_gemma":[0.9807557,0.0001183192,0.01877923,0.0001131908,0.000150747,0.00001246147,0.00004059977,0.00002274937,0.000006976591],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.484418,"threshold_uncertainty_score":0.5205344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01989699591873091,"score_gpt":0.2546096962148817,"score_spread":0.2347127002961508,"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."}}