{"id":"W2088830892","doi":"10.1007/s10822-014-9715-5","title":"Exhaustive docking and solvated interaction energy scoring: lessons learned from the SAMPL4 challenge","year":2014,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":34,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Docking (animal); Affinities; Integrase; Binding affinities; Virtual screening; Solvation; Chemistry; Binding site; Human immunodeficiency virus (HIV); Computational chemistry; Computational biology; Molecular dynamics; Computer science; Stereochemistry; Biology; Molecule","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":[0.007497211,0.00222928,0.00373553,0.001094184,0.001327761,0.002267169,0.005323414,0.002527285,0.006967407],"category_scores_gemma":[0.01465575,0.001126488,0.001366864,0.001837125,0.001290557,0.003622138,0.004169013,0.003720209,0.002197096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008805043,"about_ca_system_score_gemma":0.002255859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0079688,"about_ca_topic_score_gemma":0.01274742,"domain_scores_codex":[0.9963881,0.001838259,0.0001362886,0.0003707734,0.001021067,0.0002456544],"domain_scores_gemma":[0.9948562,0.003178905,0.0001107453,0.0009497986,0.0006810729,0.0002231295],"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":[0.001052287,0.0005515738,0.005461717,0.001022784,0.0008264231,0.0004489553,0.000304076,0.4545792,0.006298273,0.04102874,0.04956359,0.4388625],"study_design_scores_gemma":[0.0001921176,0.0001752043,0.0007464425,0.00007304962,0.00006153378,0.0001700837,0.000108673,0.928818,0.002686828,0.05851096,0.008394216,0.00006291886],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1714608,0.01326617,0.7642909,0.009745586,0.0006697152,0.0002576298,0.00178178,0.01221652,0.02631085],"genre_scores_gemma":[0.4763807,0.00435108,0.5024448,0.001922658,0.0003177993,0.0002621821,0.004031981,0.003193648,0.007095161],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0079688,"threshold_uncertainty_score":0.03964949,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03755002229013055,"score_gpt":0.2753548176473706,"score_spread":0.23780479535724,"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."}}