{"id":"W2766245239","doi":"10.1371/journal.pone.0186869","title":"A composite docking approach for the identification and characterization of ectosteric inhibitors of cathepsin K","year":2017,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Bone Metabolism and Diseases","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Institute of Musculoskeletal Health and Arthritis; National Cancer Institute; Natural Sciences and Engineering Research Council of Canada; Institute of Circulatory and Respiratory Health; Canadian Institutes of Health Research","keywords":"Cathepsin K; Collagenase; Docking (animal); Chemistry; Biochemistry; IC50; Cathepsin; Enzyme; Active site; Matrix metalloproteinase inhibitor; Cysteine protease; Protease; Osteoclast; In vitro; Medicine","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":[0.001062447,0.001845725,0.002292197,0.001605692,0.0003785888,0.0011207,0.00121242,0.0008275645,0.001818046],"category_scores_gemma":[0.001604888,0.0004478918,0.001711579,0.001483512,0.0002844151,0.0004996129,0.001085539,0.0007725423,0.0003980918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005541022,"about_ca_system_score_gemma":0.0009451052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003439452,"about_ca_topic_score_gemma":0.003982151,"domain_scores_codex":[0.9993875,0.0001971512,0.00003655437,0.00008596775,0.00020251,0.00009028401],"domain_scores_gemma":[0.9996198,0.0001466416,0.00005724932,0.00003038984,0.00008971777,0.00005620121],"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":[0.002359399,0.0007711601,0.005813901,0.0005569041,0.0009888633,0.0003766919,0.00008086336,0.8504714,0.05736275,0.003978274,0.002319333,0.07492053],"study_design_scores_gemma":[0.0001514072,0.0004648611,0.001140976,0.00001173998,0.0001349644,0.00008045725,0.00002668686,0.9900803,0.006564964,0.0005513862,0.0007576153,0.00003460235],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6690914,0.001995925,0.314778,0.00041116,0.0001183168,0.0005090229,0.001566524,0.002780325,0.0087493],"genre_scores_gemma":[0.8329455,0.0009727503,0.1605577,0.0001579628,0.00002166193,0.0005575288,0.001895657,0.0001591561,0.002732144],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003439452,"threshold_uncertainty_score":0.006838858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03072928892573467,"score_gpt":0.2364010556527552,"score_spread":0.2056717667270205,"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."}}