{"id":"W3138390968","doi":"10.1093/bioinformatics/btab188","title":"PoseFilter: a PyMOL plugin for filtering and analyzing small molecule docking in symmetric binding sites","year":2021,"lang":"en","type":"article","venue":"Bioinformatics","topic":"14-3-3 protein interactions","field":"Biochemistry, Genetics and Molecular Biology","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canada First Research Excellence Fund","keywords":"Plug-in; Docking (animal); Computer science; Graphical user interface; Software; Interface (matter); Computational biology; Data mining; Programming language; Operating system; Biology","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":[0.0001588374,0.0001393959,0.0001486237,0.0002106118,0.00008929266,0.0001098888,0.0001018948,0.0001017178,0.000007587914],"category_scores_gemma":[0.0004106133,0.0001501905,0.00007173225,0.0002809754,0.00002096581,0.0000174305,0.0001790476,0.00009292465,0.000004804971],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003264566,"about_ca_system_score_gemma":0.00005250717,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000105104,"about_ca_topic_score_gemma":0.00006227849,"domain_scores_codex":[0.9991416,0.00001873961,0.0003303238,0.0001768386,0.0000664651,0.0002659952],"domain_scores_gemma":[0.9994811,0.00004813711,0.0001106108,0.000220453,0.0000802514,0.0000594601],"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":[0.00003114207,0.0000472149,0.005595865,0.0003180602,0.00008247461,0.00001095862,0.0004278517,0.0002347155,0.9856616,0.0001863923,0.0002342672,0.007169508],"study_design_scores_gemma":[0.0009389592,0.0001547664,0.0008671859,0.0001781638,0.00003386114,0.0001144023,0.0008119721,0.02574743,0.9655187,0.00005187897,0.005184946,0.0003977747],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9714241,0.0005832306,0.02665079,0.00007865078,0.0000994394,0.0002537408,0.00003981291,0.00001677429,0.0008534638],"genre_scores_gemma":[0.9274963,0.0001708118,0.07149475,0.0001789493,0.00009160466,0.00006184159,0.0002500269,0.00002679807,0.0002289537],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04484396,"threshold_uncertainty_score":0.612459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231698618666592,"score_gpt":0.2668080451882016,"score_spread":0.2436381833215424,"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."}}