{"id":"W2897084571","doi":"10.1101/442897","title":"CANDOCK: Chemical atomic network based hierarchical flexible docking algorithm using generalized statistical potentials","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Purdue Pharma (Canada)","funders":"","keywords":"Docking (animal); Searching the conformational space for docking; Binding affinities; Affinities; Protein–ligand docking; Binding pocket; Chemical space; Chemistry; Computer science; Drug discovery; Algorithm; Molecular dynamics; Computational chemistry; Binding site; Biological system; Virtual screening; Stereochemistry; Biology; Biochemistry","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.002198548,0.0009274565,0.001171475,0.000393279,0.0004195314,0.00114977,0.002295872,0.0006571944,0.00007664652],"category_scores_gemma":[0.0004443542,0.001034326,0.0003417251,0.001091122,0.0003689336,0.0003842105,0.002754455,0.001250097,0.00004915673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009944735,"about_ca_system_score_gemma":0.004659265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001454469,"about_ca_topic_score_gemma":8.048607e-7,"domain_scores_codex":[0.9927199,0.001193023,0.001181424,0.002280081,0.001237222,0.001388293],"domain_scores_gemma":[0.9951403,0.0007529124,0.0006412237,0.00206692,0.0007220999,0.0006766099],"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":[0.0003558493,0.001103721,0.00246835,0.001079967,0.001403779,0.001160859,0.00006246967,0.5884745,0.3114304,0.08809715,0.003866866,0.0004960748],"study_design_scores_gemma":[0.0009604221,0.00003305211,0.002897973,0.0003551694,0.0001153787,2.275175e-7,1.69866e-7,0.9332443,0.05961436,0.0008406378,0.0007789975,0.001159271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2037981,0.0003148129,0.7916147,0.0001700329,0.002631159,0.0005947715,0.0002290975,0.0006428156,0.000004548859],"genre_scores_gemma":[0.3219362,0.00001046297,0.6756606,0.0004411892,0.001762802,0.00007329159,0.000002084638,0.0001122881,0.000001087178],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3447698,"threshold_uncertainty_score":0.9998871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02518808073553181,"score_gpt":0.2814134214812138,"score_spread":0.256225340745682,"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."}}