{"id":"W4413108932","doi":"10.1021/acs.jcim.5c01030","title":"FakeRotLib: Expedient Noncanonical Amino Acid Parametrization in Rosetta","year":2025,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Diabetes and Digestive and Kidney Diseases; NIH Office of the Director; National Institute on Drug Abuse; National Institutes of Health; Bundesministerium für Bildung und Forschung; Deutsche Forschungsgemeinschaft; National Institute of General Medical Sciences; German Network for Bioinformatics Infrastructure; Deutscher Akademischer Austauschdienst; Alexander von Humboldt-Stiftung","keywords":"Parametrization (atmospheric modeling); Computer science; Conformational isomerism; Amino acid; Task (project management); Artificial intelligence; Algorithm; Computational biology; Chemistry; Biology; Physics; Molecule; Engineering","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":[0.0001499126,0.0000617325,0.000103668,0.00009676856,0.00001495101,0.00002827735,0.00006952161,0.0001161825,8.494948e-7],"category_scores_gemma":[0.0001580559,0.000053091,0.00003875255,0.00008468643,0.00001737023,0.00003172333,0.00004414676,0.000119606,2.555062e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001918006,"about_ca_system_score_gemma":0.00007253047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002015846,"about_ca_topic_score_gemma":7.636057e-7,"domain_scores_codex":[0.9993575,0.000009366093,0.000411048,0.0000484579,0.0000898014,0.00008378048],"domain_scores_gemma":[0.9996688,0.000005466259,0.0001148775,0.00006146994,0.0001089115,0.00004050271],"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":[0.0006094601,0.00006557451,0.001486082,0.0001101815,0.00005892839,0.000001947941,0.0003765076,0.0291905,0.9012643,0.001753839,0.0006685909,0.06441409],"study_design_scores_gemma":[0.002962101,0.0001654263,0.0001670184,0.0001581647,0.00003053814,0.0000632232,0.0002811107,0.4579992,0.5277544,0.002100256,0.008060848,0.0002576881],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8050441,0.0003495999,0.1940012,0.0001458128,0.00006723939,0.00005097835,0.000001534714,0.000002369693,0.0003371489],"genre_scores_gemma":[0.9963661,0.0001635765,0.002882225,0.0005082273,0.00004587752,0.000001894149,0.00002334474,0.000002367088,0.000006383488],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4288087,"threshold_uncertainty_score":0.2164988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007942070734942304,"score_gpt":0.2605679317793833,"score_spread":0.252625861044441,"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."}}