{"id":"W4408840932","doi":"10.1101/2025.03.21.644685","title":"SuperMetal: A Generative AI Framework for Rapid and Precise Metal Ion Location Prediction in Proteins","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"RNA and protein synthesis mechanisms","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Metal ions in aqueous solution; Zinc finger; Chemistry; Generative grammar; Zinc; Binding site; Metal; Ion; Computer science; Nanotechnology; Computational biology; Materials science; Artificial intelligence; Biochemistry; Biology","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":[0.0008585181,0.0009015039,0.0009581105,0.0007939386,0.0004565084,0.001220367,0.002995002,0.001316353,0.004285025],"category_scores_gemma":[0.002494831,0.0007047575,0.001438157,0.0007598878,0.001075828,0.00100428,0.001592718,0.001681448,0.001154917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001175348,"about_ca_system_score_gemma":0.001162053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006854972,"about_ca_topic_score_gemma":0.01098807,"domain_scores_codex":[0.9996188,0.0001109147,0.0000159585,0.0001075066,0.0001094893,0.00003731655],"domain_scores_gemma":[0.9992352,0.0005181756,0.00004912985,0.000069675,0.00007840108,0.00004960692],"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":[0.0001220339,0.00004332135,0.001105776,0.0001393346,0.00009611125,0.0001789278,0.0001069003,0.8845567,0.005846484,0.04047593,0.005272412,0.0620561],"study_design_scores_gemma":[0.00000457785,0.000005252927,0.00002590266,0.000003567981,0.000003216168,0.00001494065,0.000002661709,0.989436,0.0003885904,0.009505317,0.0006065849,0.000003251275],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00651116,0.0002682912,0.9883834,0.0002340348,0.00002818466,0.00002696735,0.0003337903,0.002903628,0.001310561],"genre_scores_gemma":[0.3615027,0.0007033587,0.6275675,0.0006996517,0.0001515373,0.0003460445,0.002174726,0.001058094,0.005796444],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006854972,"threshold_uncertainty_score":0.01433486,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273516058129356,"score_gpt":0.2389146463489112,"score_spread":0.2261794857676176,"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."}}