{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006710885,0.0008340791,0.001351294,0.001225919,0.000814163,0.0007960245,0.002902769,0.001376471,0.003983581],"category_scores_gemma":[0.002063483,0.0005540892,0.0009409977,0.001533072,0.0005809629,0.001077579,0.001676578,0.001364648,0.0007350599],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00141892,"about_ca_system_score_gemma":0.003199527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01995932,"about_ca_topic_score_gemma":0.02549356,"domain_scores_codex":[0.9997209,0.00006259971,0.00001089259,0.00005141122,0.0001168122,0.00003751105],"domain_scores_gemma":[0.9994676,0.0002449156,0.00005114984,0.00007717336,0.000099003,0.0000602954],"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":[0.00007807658,0.00004924553,0.0006372367,0.0000703059,0.00006193673,0.00007466728,0.00002820542,0.9125825,0.001602915,0.01491334,0.004406595,0.06549499],"study_design_scores_gemma":[0.0000190526,0.00001019201,0.00004288816,0.000002445839,0.000004135654,0.00001207967,0.000003601215,0.9950015,0.0002819464,0.004062936,0.0005539744,0.000005233669],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02327217,0.0003029404,0.9678867,0.000249346,0.00005837133,0.0001537303,0.000502512,0.004156812,0.003417419],"genre_scores_gemma":[0.284455,0.0002937626,0.7075847,0.0003019049,0.00003160056,0.0005500926,0.001531054,0.0008518002,0.00440009],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01995932,"threshold_uncertainty_score":0.03968632,"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."}}