{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001161739,0.001572707,0.001759692,0.0005353671,0.001071585,0.002081838,0.003395475,0.00199874,0.0116288],"category_scores_gemma":[0.004175814,0.001101034,0.001551201,0.0007270378,0.0005426161,0.002652285,0.001719212,0.003515393,0.005656125],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008072309,"about_ca_system_score_gemma":0.001974888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003430086,"about_ca_topic_score_gemma":0.007676056,"domain_scores_codex":[0.9995199,0.0001444965,0.00002701947,0.0001009679,0.0001415765,0.00006599985],"domain_scores_gemma":[0.999154,0.0003980366,0.00005174947,0.0002415906,0.00008043999,0.00007430223],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001338829,0.0006396272,0.008128652,0.001822284,0.001062831,0.001050976,0.0007100726,0.5300325,0.05443814,0.08688958,0.1073511,0.2065354],"study_design_scores_gemma":[0.0001325222,0.0000531574,0.0003093235,0.00005268319,0.00003930436,0.0001643771,0.0000627613,0.94686,0.01052801,0.01627867,0.02544785,0.00007153334],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1111532,0.002288918,0.7300361,0.001336537,0.0005352916,0.0002721505,0.003820338,0.1308748,0.01968257],"genre_scores_gemma":[0.3402769,0.001388504,0.6111158,0.001606974,0.0001264269,0.001122914,0.008180743,0.0290767,0.007105133],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0116288,"threshold_uncertainty_score":0.03890222,"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."}}