{"id":"W1999519914","doi":"10.1021/ci200598m","title":"Numerical Errors and Chaotic Behavior in Docking Simulations","year":2012,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Ontario Institute for Cancer Research","funders":"","keywords":"Docking (animal); Virtual screening; Protein–ligand docking; Computer science; Chaotic; Algorithm; Artificial intelligence; Molecular dynamics; Chemistry; Computational chemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003131887,0.0004078195,0.0006437117,0.00114052,0.000721648,0.001115463,0.000611063,0.0006804077,0.001135365],"category_scores_gemma":[0.04159798,0.0004428544,0.000443149,0.0006923262,0.00184414,0.001245594,0.001292133,0.001045528,0.0001709034],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001158912,"about_ca_system_score_gemma":0.000772878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003672825,"about_ca_topic_score_gemma":0.001710565,"domain_scores_codex":[0.998334,0.0006146764,0.0001347821,0.0001996975,0.0005489595,0.0001678145],"domain_scores_gemma":[0.9789445,0.01563036,0.001841069,0.00188808,0.001237405,0.0004586056],"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":[0.0002163094,0.00004738894,0.01325766,0.00007540562,0.00005341085,0.0002376705,0.0002474845,0.96469,0.003508809,0.01114905,0.0006373812,0.005879384],"study_design_scores_gemma":[0.00002033497,0.00004017762,0.002171918,0.00001857546,0.000009509445,0.00004872209,0.00004357876,0.987412,0.001923654,0.007924957,0.0003621926,0.00002436448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.900458,0.0006207039,0.08846399,0.001434772,0.0001641674,0.00009601489,0.0002165,0.001025031,0.007520831],"genre_scores_gemma":[0.9929132,0.00008757589,0.006397564,0.00005860523,0.00001294293,0.00003766915,0.00008497071,0.0001054369,0.0003021119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003672825,"threshold_uncertainty_score":0.01656318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05000159485368189,"score_gpt":0.3394582415320608,"score_spread":0.2894566466783789,"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."}}