{"id":"W2088320251","doi":"10.1016/j.jmgm.2014.02.007","title":"Designing of multi-targeted molecules using combination of molecular screening and in silico drug cardiotoxicity prediction approaches","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":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"Canadian Institutes of Health Research","keywords":"In silico; Computational biology; Docking (animal); Chemistry; Carbonic anhydrase; Isozyme; Cardiotoxicity; Drug; Combinatorial chemistry; Biochemistry; Pharmacology; Enzyme; Biology; Gene; Medicine; Toxicity; Organic chemistry","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":[0.0008497121,0.0001377253,0.0002718985,0.000281288,0.0000473406,0.00001482306,0.00006094135,0.0001395232,2.579462e-7],"category_scores_gemma":[0.00006873902,0.0001409705,0.0001270849,0.0001521532,0.00009391355,0.00002046159,0.00003772824,0.0001688564,5.979623e-9],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007262284,"about_ca_system_score_gemma":0.00002727239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002358782,"about_ca_topic_score_gemma":0.000002923586,"domain_scores_codex":[0.9987882,0.000176797,0.0005058115,0.0001878825,0.0002145148,0.0001268391],"domain_scores_gemma":[0.9991708,0.00001697979,0.000411737,0.0001130211,0.0002209362,0.00006650235],"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":[0.00007384238,0.00005762104,0.001618929,0.00005442095,0.00005127443,0.00000190779,0.00007704425,0.2384436,0.7590021,0.0002759983,0.000001171656,0.0003420722],"study_design_scores_gemma":[0.000876208,0.0001596946,0.0002606148,0.0001272853,0.00004780418,0.00002095389,0.00009973318,0.3920402,0.6060083,0.0002670971,0.000007005995,0.00008512676],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5152209,0.001662127,0.4830135,0.00000992029,0.0000200022,0.00006121803,0.000002176755,0.000001348722,0.000008829874],"genre_scores_gemma":[0.9681096,0.0004220759,0.03138405,0.00003170279,0.00002002432,0.000001408314,0.00001241636,0.00001753576,0.000001183208],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4528887,"threshold_uncertainty_score":0.5748612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03234236996232408,"score_gpt":0.2283765444012317,"score_spread":0.1960341744389076,"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."}}