{"id":"W3214234430","doi":"10.1039/d1sc05579h","title":"Automated discovery of noncovalent inhibitors of SARS-CoV-2 main protease by consensus Deep Docking of 40 billion small molecules","year":2021,"lang":"en","type":"article","venue":"Chemical Science","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Dell Technologies; Vancouver Coastal Health Research Institute; Fondazione Zegna; Michael Smith Health Research BC; VGH and UBC Hospital Foundation","keywords":"Drug discovery; Docking (animal); Computer science; AutoDock; Automation; Virtual screening; Artificial intelligence; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computational biology; Coronavirus disease 2019 (COVID-19); Machine learning; Chemistry; Bioinformatics; Engineering; Biology; In silico; Biochemistry; Medicine; Infectious disease (medical specialty)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000718638,0.0008632179,0.001105868,0.0005928122,0.0003162225,0.0008313765,0.0007655702,0.0004703153,0.001842922],"category_scores_gemma":[0.001041751,0.0003479105,0.0006493643,0.000540285,0.0002734061,0.0005586569,0.0009839133,0.0007287182,0.0005361611],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007116907,"about_ca_system_score_gemma":0.001197467,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001931336,"about_ca_topic_score_gemma":0.004302986,"domain_scores_codex":[0.9995459,0.0001108022,0.00002740775,0.00007747822,0.0001683809,0.0000699896],"domain_scores_gemma":[0.9998021,0.0000656656,0.00003276081,0.00003700626,0.00003844814,0.00002396743],"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":[0.001133435,0.0008690542,0.007515512,0.0004214829,0.0003423937,0.0002434884,0.00008599067,0.505914,0.1889588,0.006133848,0.00490708,0.283475],"study_design_scores_gemma":[0.0001212735,0.0005506458,0.001039742,0.000009392263,0.00005561057,0.00008028508,0.00003184575,0.9147021,0.07929651,0.001704276,0.002385868,0.00002247573],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7966073,0.001854763,0.185567,0.0003471118,0.00004462377,0.0003256792,0.001073612,0.005723403,0.008456565],"genre_scores_gemma":[0.9047012,0.0006496381,0.09019427,0.0002035354,0.000009551561,0.0001505512,0.001748029,0.0001661761,0.002176931],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001931336,"threshold_uncertainty_score":0.006165147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894277826203139,"score_gpt":0.3079937382215698,"score_spread":0.2790509599595384,"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."}}