{"id":"W3094451304","doi":"10.1080/07391102.2020.1835721","title":"High-throughput virtual screening of drug databanks for potential inhibitors of SARS-CoV-2 spike glycoprotein","year":2020,"lang":"en","type":"article","venue":"Journal of Biomolecular Structure and Dynamics","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"ADME; Silodosin; Virtual screening; Molecular dynamics; In silico; Docking (animal); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Chemistry; Coronavirus disease 2019 (COVID-19); Protein subunit; Ligand (biochemistry); Computational biology; Biophysics; Pharmacology; Drug; Medicine; Biology; Receptor; Biochemistry; Computational chemistry; Internal medicine","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":[0.0003150608,0.0001720119,0.0004263687,0.0001568046,0.00004977649,0.00006067132,0.0005259658,0.00007775828,9.290664e-7],"category_scores_gemma":[0.0001097121,0.0001516916,0.0001989594,0.000360376,0.0001018657,0.0004265771,0.0002914181,0.0001909105,6.165153e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002234409,"about_ca_system_score_gemma":0.0001436985,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003180009,"about_ca_topic_score_gemma":0.000004278815,"domain_scores_codex":[0.9983165,0.0001102243,0.0006855299,0.0002431625,0.0004847774,0.0001597846],"domain_scores_gemma":[0.9985498,0.00009216878,0.0007761851,0.000200382,0.0003022965,0.00007919544],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000465897,0.00006247382,0.000139846,0.0002386398,0.0003084518,0.0001050735,0.0009613172,0.04146063,0.8344714,0.04697986,0.0002714907,0.07453491],"study_design_scores_gemma":[0.001612382,0.0007216487,0.0004364107,0.00009962927,0.00008476178,0.0001425679,0.00008860866,0.5364383,0.4475746,0.01245749,0.0001109153,0.0002327432],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4090936,0.0001430381,0.5898026,0.0005547698,0.0002159357,0.00009475982,0.0000886902,0.000004671554,0.000001888504],"genre_scores_gemma":[0.649892,0.000007949414,0.3498159,0.0001556087,0.0001050453,2.994823e-7,0.00001310742,0.000009415371,7.046825e-7],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4949776,"threshold_uncertainty_score":0.6185805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01438568179715265,"score_gpt":0.2774774392741691,"score_spread":0.2630917574770164,"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."}}