{"id":"W3173815633","doi":"10.1039/d1ra02293h","title":"Antiviral nanoparticle ligands identified with datamining and high-throughput virtual screening","year":2021,"lang":"en","type":"article","venue":"RSC Advances","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"German Network for Bioinformatics Infrastructure; Bundesministerium für Bildung und Forschung; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Deutsche Forschungsgemeinschaft","keywords":"Virtual screening; Throughput; Nanoparticle; High-throughput screening; Chemistry; Nanotechnology; Computational biology; Combinatorial chemistry; Drug discovery; Computer science; Materials science; Biology; Biochemistry; Operating system","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":[0.001908472,0.001468739,0.002267177,0.00179376,0.0007562641,0.002923612,0.001129158,0.001112563,0.004516953],"category_scores_gemma":[0.0033358,0.0004473696,0.001845466,0.001322122,0.0004726749,0.001461316,0.001181386,0.001409671,0.001869417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001172987,"about_ca_system_score_gemma":0.002056696,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001556006,"about_ca_topic_score_gemma":0.003526815,"domain_scores_codex":[0.9989212,0.0002759299,0.00006607466,0.0001885806,0.0004232592,0.0001250092],"domain_scores_gemma":[0.9993308,0.0002563696,0.00009694068,0.000102834,0.0001567915,0.00005631671],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003183654,0.001926415,0.03048985,0.006904176,0.002114024,0.001507027,0.0002635187,0.153571,0.2442582,0.01262053,0.05063898,0.4925227],"study_design_scores_gemma":[0.0008070363,0.002356368,0.007737481,0.0008433207,0.001771049,0.002299281,0.0005457526,0.4844089,0.3053013,0.0192029,0.1743333,0.0003931227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6465483,0.04559352,0.2111359,0.007354822,0.00147692,0.001975554,0.02629763,0.01037324,0.04924412],"genre_scores_gemma":[0.7887641,0.01581455,0.1545876,0.002363353,0.0001548383,0.0009485205,0.02890881,0.0004955034,0.007962702],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004516953,"threshold_uncertainty_score":0.01511073,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02108983584833762,"score_gpt":0.2956552331321505,"score_spread":0.2745653972838129,"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."}}