{"id":"W2589852329","doi":"10.1155/2017/6397836","title":"Investigations on Binding Pattern of Kinase Inhibitors with PPAR<i>γ</i>: Molecular Docking, Molecular Dynamic Simulations, and Free Energy Calculation Studies","year":2017,"lang":"en","type":"article","venue":"PPAR Research","topic":"Peroxisome Proliferator-Activated Receptors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Foreign Affairs and International Trade Canada; Canadian Bureau for International Education; Natural Sciences and Engineering Research Council of Canada; College of Pharmacy and Nutrition, University of Saskatchewan; King Saud University; University of Saskatchewan","keywords":"In silico; Docking (animal); Molecular dynamics; Peroxisome proliferator-activated receptor; Computational biology; Kinase; In vitro; In vivo; Chemistry; Binding site; Biochemistry; Pharmacology; Biology; Receptor; Medicine; Computational chemistry; Genetics; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003841094,0.0002067894,0.0002160887,0.0002514608,0.0005435435,0.00009608809,0.0003666196,0.000159993,0.000004431301],"category_scores_gemma":[0.000840569,0.0001841528,0.00005025703,0.0001732727,0.0006219755,0.0000229756,0.0004375058,0.0002306601,0.000002133095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000575774,"about_ca_system_score_gemma":0.0001118775,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001413668,"about_ca_topic_score_gemma":0.000184908,"domain_scores_codex":[0.9980895,0.0002465792,0.0002523809,0.0005126489,0.0005355363,0.0003633919],"domain_scores_gemma":[0.9982744,0.00005643758,0.0001750476,0.0009419151,0.0004055559,0.0001466198],"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":[0.00006029732,0.00006519983,0.01522258,0.00005341174,0.0001807407,0.0000138569,0.0001138411,0.002491393,0.9798534,0.0002649778,0.0001447646,0.001535533],"study_design_scores_gemma":[0.0007027682,0.0003768707,0.0006706342,0.0001494264,0.00001963446,0.000003972934,0.0001125132,0.001711807,0.9949914,0.000328434,0.000721335,0.00021126],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9974689,0.001040522,0.0005362072,0.0004303563,0.00005331384,0.0002596191,0.00004256305,0.00001296871,0.0001555513],"genre_scores_gemma":[0.9991351,0.0002210414,0.0002742166,0.00005088681,0.00005177809,0.00005284939,0.0001066462,0.00004559364,0.00006184203],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01513794,"threshold_uncertainty_score":0.7509535,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04453122265280678,"score_gpt":0.3715829945861627,"score_spread":0.3270517719333559,"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."}}