{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004395937,0.001094291,0.001252344,0.0005150975,0.0002597433,0.0006908018,0.0006213084,0.0005279069,0.001685888],"category_scores_gemma":[0.0005717368,0.0005325475,0.0008284489,0.0003072013,0.0001752619,0.0004643848,0.0004423039,0.0006064557,0.0005038156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004147431,"about_ca_system_score_gemma":0.0005378157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007197525,"about_ca_topic_score_gemma":0.001486017,"domain_scores_codex":[0.999821,0.00004081848,0.00001354108,0.00004127606,0.00005635337,0.00002697461],"domain_scores_gemma":[0.9998609,0.00005184241,0.00002900604,0.00001464888,0.00002841944,0.00001522472],"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":[0.000379644,0.0003983555,0.002756207,0.0004972318,0.00019539,0.0004318338,0.00005279575,0.3595845,0.5496974,0.00315528,0.000731568,0.08211974],"study_design_scores_gemma":[0.00008512108,0.0005672009,0.0007462435,0.00001529535,0.0001787954,0.0001931811,0.00002695052,0.7691635,0.2254404,0.0008869076,0.002660085,0.00003631645],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.4314857,0.00155973,0.5576186,0.0002629905,0.00005354342,0.0006777562,0.0004929072,0.002677249,0.005171632],"genre_scores_gemma":[0.7508268,0.001261504,0.2450042,0.0001464123,0.00001029722,0.0004107507,0.0005691038,0.0001524207,0.001618522],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.001685888,"threshold_uncertainty_score":0.005639851,"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."}}