{"id":"W4250402082","doi":"10.26434/chemrxiv.11860077.v1","title":"Rapid Identification of Potential Inhibitors of SARS-CoV-2 Main Protease by Deep Docking of 1.3 Billion Compounds","year":2020,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Docking (animal); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); Coronavirus; 2019-20 coronavirus outbreak; Workflow; Drug discovery; Protease; Drug development; Investigational Drugs; Approved drug; Drug; Computational biology; Medicine; Infectious disease (medical specialty); Virology; Computer science; Chemistry; Pharmacology; Outbreak; Bioinformatics; Disease; Biology; Clinical trial; Enzyme; Veterinary medicine","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":[0.000487256,0.001638589,0.001384767,0.0009058983,0.0003227032,0.001079138,0.0008795332,0.0009976598,0.00421301],"category_scores_gemma":[0.001166927,0.0003744321,0.001220059,0.0009512329,0.0002752812,0.000683575,0.0008461432,0.0008472581,0.001377393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005837067,"about_ca_system_score_gemma":0.0009148024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004630112,"about_ca_topic_score_gemma":0.006633791,"domain_scores_codex":[0.9997008,0.0000616121,0.00001683613,0.00005342863,0.00009954176,0.00006790269],"domain_scores_gemma":[0.9998217,0.0000595941,0.00002259468,0.00003398466,0.00003458337,0.00002752737],"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":[0.001136467,0.0009652147,0.008188055,0.001232483,0.0007632157,0.0003176272,0.00005922894,0.7364136,0.03356967,0.003744481,0.02871822,0.1848918],"study_design_scores_gemma":[0.0002150289,0.0004165687,0.0009525127,0.00002690748,0.00008951857,0.00007274497,0.00002786873,0.9770619,0.01381028,0.002514395,0.004789295,0.00002310527],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7858657,0.01249968,0.1326455,0.002178793,0.0004582868,0.0004069013,0.01127992,0.02098039,0.03368478],"genre_scores_gemma":[0.8672302,0.003409619,0.1024808,0.001127483,0.00008179445,0.0002724779,0.0161525,0.0005789885,0.008666134],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004630112,"threshold_uncertainty_score":0.01409394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04265545864559501,"score_gpt":0.3089891695133022,"score_spread":0.2663337108677072,"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."}}