{"id":"W2031560630","doi":"10.1021/jm060961+","title":"Progressive Docking:  A Hybrid QSAR/Docking Approach for Accelerating In Silico High Throughput Screening","year":2006,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"Child and Family Research Institute; Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"","keywords":"Docking (animal); Chemistry; Quantitative structure–activity relationship; In silico; Virtual screening; Computational biology; Drug discovery; Stereochemistry; Biochemistry; Biology","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":[0.0009214792,0.001326177,0.001133773,0.0009412774,0.0002536103,0.0004798344,0.001605689,0.000574972,0.003135921],"category_scores_gemma":[0.001198565,0.0005135738,0.001065825,0.001106051,0.0003861162,0.0006888122,0.001254541,0.0008277908,0.0009931939],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000399959,"about_ca_system_score_gemma":0.0006873751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003867535,"about_ca_topic_score_gemma":0.004612266,"domain_scores_codex":[0.9995629,0.0001415135,0.00001994935,0.00005127663,0.0001843664,0.00004003976],"domain_scores_gemma":[0.9996529,0.0001688352,0.00002521109,0.00007628127,0.00005290253,0.00002389448],"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":[0.0003029069,0.0002239254,0.001185996,0.0002329484,0.0002509999,0.0002228321,0.0000589365,0.7516631,0.03395114,0.007739407,0.002233371,0.2019344],"study_design_scores_gemma":[0.0001028133,0.0002404098,0.0004803722,0.000009642479,0.00005805878,0.0001355209,0.000008058446,0.9829283,0.009508858,0.003431548,0.003060125,0.00003627852],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0319023,0.0003602532,0.9596996,0.0001111073,0.00003435936,0.0001909781,0.0002798746,0.004871588,0.002550028],"genre_scores_gemma":[0.3067239,0.0008416848,0.6884156,0.0001334341,0.00002771901,0.0003760471,0.0008211599,0.0003515535,0.002309015],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003867535,"threshold_uncertainty_score":0.01049072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0283123438173512,"score_gpt":0.3029628115624223,"score_spread":0.2746504677450711,"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."}}