{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008923341,0.0001549805,0.0003163526,0.0001289323,0.00004907205,0.00006378713,0.0009480576,0.00005783452,0.000001475388],"category_scores_gemma":[0.001416614,0.0001400894,0.00011815,0.001842899,0.0009114382,0.0003189766,0.00079061,0.0001108227,9.840326e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093556,"about_ca_system_score_gemma":0.0006354137,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006348539,"about_ca_topic_score_gemma":0.000002238067,"domain_scores_codex":[0.9975439,0.0001506885,0.0005988857,0.0005826075,0.0008144496,0.00030944],"domain_scores_gemma":[0.9981089,0.0005707539,0.0003693876,0.0004889219,0.0003831771,0.00007884941],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001538558,0.0001758154,0.0002305264,0.00009428961,0.000006177632,0.00001137007,0.000178621,0.00230049,0.9929851,0.002132327,0.00006144478,0.001808456],"study_design_scores_gemma":[0.000178823,0.00003039584,0.0003654035,0.0001315429,0.000004843548,0.00001647882,0.00002418991,0.2447007,0.7526323,0.001791931,0.000009404373,0.000113949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8571708,0.0001167381,0.1420056,0.0002016309,0.000127197,0.0001570569,0.00002067109,0.00004992572,0.0001503096],"genre_scores_gemma":[0.893351,0.000003363821,0.1065665,0.00004869144,0.00001086012,0.000006059564,0.000003972332,0.000005912398,0.000003633256],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2424002,"threshold_uncertainty_score":0.5712681,"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."}}