{"id":"W4210617107","doi":"10.1038/s41596-021-00659-2","title":"Artificial intelligence–enabled virtual screening of ultra-large chemical libraries with deep docking","year":2022,"lang":"en","type":"review","venue":"Nature Protocols","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":389,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Canadian Institutes of Health Research; Michael Smith Health Research BC; Vancouver Coastal Health Research Institute; Fondazione Zegna","keywords":"Virtual screening; Computer science; Workflow; Chemical database; Docking (animal); Drug discovery; Bioinformatics; Database; Biology","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001350226,0.0005998004,0.001610393,0.0004156485,0.0002186762,0.0004231948,0.002876613,0.0005743895,0.0001128807],"category_scores_gemma":[0.0005650067,0.0004755524,0.0004775326,0.002146966,0.0001197077,0.0007541152,0.001006405,0.002666109,0.000006386153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001279966,"about_ca_system_score_gemma":0.001137248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002076617,"about_ca_topic_score_gemma":9.421124e-7,"domain_scores_codex":[0.9952289,0.0007790488,0.00110408,0.001046945,0.001291223,0.0005497792],"domain_scores_gemma":[0.9958344,0.002111191,0.0009021505,0.0008896672,0.000133325,0.0001292986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004422773,0.000121913,0.000001378929,0.002504997,0.0001057561,0.00002940732,0.0001686596,0.001002294,0.000001534584,0.1480833,0.00002463518,0.8479119],"study_design_scores_gemma":[0.0001243568,0.0002812906,6.204226e-7,0.006904732,0.00009947501,0.0001029315,0.00003619826,0.009963805,0.0008209635,0.01256676,0.9683126,0.00078631],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[4.677633e-7,0.2942228,0.6619292,0.0000258919,0.0001332767,0.04303465,0.00003232598,0.0001489969,0.0004723677],"genre_scores_gemma":[0.00007877973,0.102964,0.6366185,0.0002584874,0.000762991,0.2587235,0.0003044108,0.0002331017,0.00005628061],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9682879,"threshold_uncertainty_score":0.9997696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07732661057771312,"score_gpt":0.3948962207137753,"score_spread":0.3175696101360622,"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."}}