{"id":"W4310470802","doi":"10.1101/2022.11.30.518473","title":"DAPTEV: Deep aptamer evolutionary modelling for COVID-19 drug design","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced biosensing and bioanalysis techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; Brock University","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; Brock University; Howard Hughes Medical Institute","keywords":"Aptamer; Systematic evolution of ligands by exponential enrichment; In silico; Computational biology; Drug discovery; Coronavirus disease 2019 (COVID-19); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Small molecule; Oligonucleotide; Binding affinities; Biology; Computer science; RNA; DNA; Bioinformatics; Genetics; Medicine; Gene; 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":[0.0005202772,0.0007089938,0.0007008467,0.0004798199,0.0002988928,0.0005901291,0.0009033469,0.001068776,0.002999455],"category_scores_gemma":[0.001028382,0.0003493232,0.0008343693,0.0002737556,0.0003437288,0.0002876903,0.0005895481,0.0008848479,0.0002338255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006350142,"about_ca_system_score_gemma":0.000966472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003972477,"about_ca_topic_score_gemma":0.003715513,"domain_scores_codex":[0.9998711,0.00005337386,0.000005059004,0.0000187764,0.000027514,0.00002419376],"domain_scores_gemma":[0.9996496,0.0002470445,0.0000212164,0.00001275492,0.00004158695,0.00002778654],"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":[0.00003552825,0.00002521123,0.000713323,0.00003847863,0.00002656617,0.0000485969,0.0000145692,0.9871437,0.00115824,0.002682358,0.0004478444,0.007665633],"study_design_scores_gemma":[0.000007356356,0.000009208031,0.00002653141,0.000001708669,0.000002254232,0.000003745628,0.000002517664,0.9988816,0.0002074093,0.0005501637,0.0003061983,0.000001210801],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2825309,0.000950414,0.6956745,0.001025469,0.0001891822,0.0002429813,0.0008618068,0.002112421,0.01641232],"genre_scores_gemma":[0.7971246,0.0003466871,0.1969808,0.000289987,0.00003671407,0.0004497969,0.0007720909,0.0001848316,0.003814522],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003972477,"threshold_uncertainty_score":0.0100342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02649428685622456,"score_gpt":0.2649744434335046,"score_spread":0.23848015657728,"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."}}