{"id":"W4400007706","doi":"10.1038/s41592-024-02321-7","title":"Outcomes of the EMDataResource cryo-EM Ligand Modeling Challenge","year":2024,"lang":"en","type":"article","venue":"Nature Methods","topic":"Advanced Electron Microscopy Techniques and Applications","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Biotechnology and Biological Sciences Research Council; National Institute of General Medical Sciences; Medical Research Council; Science and Engineering Research Board; U.S. Department of Health and Human Services; National Institutes of Health; Wellcome Trust; Deutsche Forschungsgemeinschaft","keywords":"Resolution (logic); Ligand (biochemistry); Cryo-electron microscopy; Nucleic acid; Atomic model; Macromolecule; Computational biology; Crystallography; Chemistry; Biophysics; Biology; Biochemistry; Computer science; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0002426255,0.00009880999,0.0001050803,0.00002070544,0.00005361917,0.00001257535,0.0002463752,0.0002345825,0.000003424339],"category_scores_gemma":[0.00006441896,0.00006447877,0.0001072309,0.0001162693,0.00002906699,0.000002139605,0.0001190679,0.0003303027,5.413745e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008104787,"about_ca_system_score_gemma":0.00002810606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002105262,"about_ca_topic_score_gemma":0.000004612349,"domain_scores_codex":[0.9993987,0.00005217666,0.0001246309,0.0002337806,0.00006565084,0.0001250398],"domain_scores_gemma":[0.9994807,0.00002245578,0.00002847339,0.0004100675,0.00003766689,0.00002059922],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005634706,0.00001631805,0.00003836877,0.00002521059,0.00003597232,1.824238e-7,0.00004797123,0.000182479,0.9722775,0.002797962,0.0007262633,0.02384619],"study_design_scores_gemma":[0.0000436621,0.00003140826,0.00004664495,0.00001729641,0.00001965498,0.000003294595,0.00002477488,0.001705481,0.8212932,0.003231402,0.1734949,0.00008817517],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010142,0.02423467,0.8725691,0.0009100099,0.0001604921,0.0002141877,0.00003572119,0.00004959686,0.0008119798],"genre_scores_gemma":[0.7615538,0.0005191896,0.2365962,0.0003073333,0.000111709,0.00003017276,0.00002281933,0.00002651662,0.0008323319],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6605396,"threshold_uncertainty_score":0.2629368,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01354148707812372,"score_gpt":0.4202103676769794,"score_spread":0.4066688805988557,"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."}}