{"id":"W4386352296","doi":"10.1101/2023.08.30.555318","title":"Benchmarking of PROTAC docking and virtual screening tools","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Degradation and Inhibitors","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Structural Genomics Consortium; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Genentech; Alliance de recherche numérique du Canada; Québec Consortium for Drug Discovery; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Mitacs; Ontario Genomics; Genome Canada; McGill University; Bayer; Pfizer; Bristol-Myers Squibb","keywords":"Virtual screening; Docking (animal); Computer science; Ternary complex; Protein degradation; Benchmarking; Computational biology; Drug discovery; Bioinformatics; Chemistry; Biology; Biochemistry; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003648557,0.002270085,0.001511931,0.001904717,0.0008503416,0.001654369,0.002874997,0.001329471,0.005601058],"category_scores_gemma":[0.004523055,0.0005197969,0.001342058,0.001978447,0.0006556023,0.0008357729,0.001769515,0.0009876611,0.00275894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008616787,"about_ca_system_score_gemma":0.001256875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004826427,"about_ca_topic_score_gemma":0.002937826,"domain_scores_codex":[0.9969703,0.00117369,0.0002230197,0.0004411231,0.0008618407,0.000329875],"domain_scores_gemma":[0.9973138,0.001249717,0.000107338,0.0004827804,0.0006354786,0.0002109123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004351433,0.001090083,0.006378619,0.001087883,0.0008272565,0.0006636386,0.000175501,0.8427788,0.01412639,0.007540461,0.03512922,0.08585069],"study_design_scores_gemma":[0.0003110551,0.0004559853,0.00117787,0.00003440521,0.00004874763,0.0001213442,0.00007015857,0.9775321,0.01230425,0.001623831,0.006256354,0.00006387084],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7355309,0.003075473,0.1403686,0.001037203,0.0007906921,0.0008338381,0.02044994,0.06609695,0.03181644],"genre_scores_gemma":[0.8645654,0.0008090302,0.09683225,0.0003467339,0.00004611195,0.0007634001,0.03062405,0.002148195,0.00386492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005601058,"threshold_uncertainty_score":0.01929563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02236105086687406,"score_gpt":0.2348464171478982,"score_spread":0.2124853662810241,"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."}}