{"id":"W2553944050","doi":"10.1021/acs.jcim.6b00400","title":"Cheminformatics Modeling of Adverse Drug Responses by Clinically Relevant Mutants of Human Androgen Receptor","year":2016,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Cancer Institute; Canadian Cancer Society Research Institute","keywords":"Androgen receptor; Enzalutamide; Cheminformatics; Antiandrogens; Computational biology; Prostate cancer; Mutant; In silico; Bicalutamide; DrugBank; Transcription factor; Drug repositioning; Drug; Biology; Bioinformatics; Chemistry; Medicine; Genetics; Pharmacology; Cancer; Gene","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":[0.0006129859,0.001035489,0.0009619971,0.0006429162,0.0003834661,0.0008937944,0.0008632458,0.001320407,0.004145492],"category_scores_gemma":[0.0009831374,0.0003894267,0.00138628,0.0006144974,0.0003633252,0.0003513989,0.0005113332,0.0009390953,0.0004334218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000842139,"about_ca_system_score_gemma":0.001789632,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01288646,"about_ca_topic_score_gemma":0.009467138,"domain_scores_codex":[0.9998024,0.00006772643,0.000009894663,0.00002848374,0.00005725928,0.00003429529],"domain_scores_gemma":[0.9992798,0.0005483734,0.00005266255,0.00001916636,0.00007565623,0.00002437409],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003168884,0.00002490827,0.000549353,0.00007106854,0.00002369664,0.00004020833,0.000006834617,0.9959502,0.0004071404,0.001046766,0.0002632928,0.001584776],"study_design_scores_gemma":[0.00001313704,0.0000328551,0.0001170117,0.000007314077,0.00001152228,0.000009729784,0.000006306379,0.9983445,0.0003075809,0.0006164136,0.0005301426,0.000003558965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6342486,0.007578079,0.2904342,0.003123496,0.0003382663,0.0004232711,0.01114554,0.001930444,0.05077805],"genre_scores_gemma":[0.9441535,0.002341508,0.04309224,0.0004528726,0.00006017439,0.000532578,0.003772801,0.0001005898,0.005493667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01288646,"threshold_uncertainty_score":0.0256229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04440772685249501,"score_gpt":0.3498515790777051,"score_spread":0.3054438522252101,"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."}}