{"id":"W2129414710","doi":"10.1002/prot.20551","title":"Assessment of CAPRI predictions in rounds 3–5 shows progress in docking procedures","year":2005,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":347,"is_retracted":false,"has_abstract":true,"ca_institutions":"Hospital for Sick Children","funders":"","keywords":"Docking (animal); Computational biology; Macromolecular docking; Computer science; Protein–ligand docking; Searching the conformational space for docking; Protein structure; Artificial intelligence; Chemistry; Virtual screening; Biology; Biochemistry; Drug discovery; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003061071,0.0001323529,0.0001686296,0.0003569536,0.00006173711,0.0001048558,0.0001911459,0.00007490948,0.00001088703],"category_scores_gemma":[0.0000411006,0.0001117804,0.00002752993,0.0005628372,0.00006330446,0.001046909,0.0001301258,0.0002062836,4.134219e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001054074,"about_ca_system_score_gemma":0.0002071419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001053949,"about_ca_topic_score_gemma":0.0002688554,"domain_scores_codex":[0.9988012,0.0000520406,0.0004925939,0.0001617027,0.0003140953,0.0001783447],"domain_scores_gemma":[0.9994702,0.00004590718,0.0001828929,0.00018149,0.0000736182,0.00004589953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004787719,0.0002975024,0.2232567,0.0008967955,0.00004790371,0.000002137309,0.006302358,0.2321007,0.0003393818,0.1649061,0.00007061819,0.3717319],"study_design_scores_gemma":[0.0004228184,0.00007943529,0.2888488,0.00006596048,0.000003486869,0.00001245192,0.0001335003,0.7065312,0.0001445449,0.003473906,0.0001821906,0.0001017504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2964485,0.000143858,0.7011347,0.0004621104,0.0001922054,0.0007253602,0.00001233167,0.00005895004,0.0008219556],"genre_scores_gemma":[0.7118155,0.000005692716,0.2880321,0.00006853419,0.0000322942,0.00002886376,0.000006445594,0.000003162663,0.000007370596],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4744305,"threshold_uncertainty_score":0.4558273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01133100212198269,"score_gpt":0.2838555535211666,"score_spread":0.272524551399184,"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."}}