{"id":"W2227961447","doi":"","title":"Investigating androgen receptor antagonism by structural analyses, molecular dynamics simulations and support vector machines","year":2007,"lang":"en","type":"article","venue":"Cancer Research","topic":"Prostate Cancer Treatment and Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Androgen receptor; Agonist; Chemistry; Antagonist; Prostate cancer; Antiandrogen; Receptor; Androgen; Pharmacology; Internal medicine; Biology; Hormone; Cancer; Medicine; Biochemistry","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":[0.0005691034,0.0007842986,0.0008185265,0.0004274762,0.0003345199,0.0003864438,0.0005410716,0.0005821677,0.002143217],"category_scores_gemma":[0.0007613001,0.0003499366,0.0005088091,0.0003407883,0.0002562487,0.0004616646,0.0002881091,0.0005546644,0.0003061529],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004498589,"about_ca_system_score_gemma":0.0005496062,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004222275,"about_ca_topic_score_gemma":0.004998695,"domain_scores_codex":[0.9998955,0.0000423703,0.000005476563,0.00001263232,0.00003026031,0.00001374112],"domain_scores_gemma":[0.999707,0.0001888215,0.00002411259,0.00001427293,0.00004493441,0.00002086641],"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":[0.0001144776,0.0001347159,0.001240566,0.00008316839,0.00008357548,0.00005607558,0.00002984261,0.9616106,0.004996144,0.002157881,0.001187047,0.02830588],"study_design_scores_gemma":[0.00000496455,0.00001288448,0.00006435977,0.000001051322,0.000002538078,0.000002024783,0.000003168047,0.9990509,0.0004521497,0.0002239147,0.0001807399,0.000001282291],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6685609,0.002258389,0.3120019,0.001213401,0.0002380393,0.00020679,0.0005572721,0.002117625,0.01284562],"genre_scores_gemma":[0.8512708,0.0009840894,0.1435727,0.00009722669,0.00004809583,0.0002845473,0.0006293066,0.0001186696,0.002994563],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004222275,"threshold_uncertainty_score":0.008395374,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07754643704947568,"score_gpt":0.4658515299322195,"score_spread":0.3883050928827438,"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."}}