{"id":"W7114907792","doi":"10.1093/bib/bbaf631.068","title":"Prediction of protein binding residues for metal ions, nucleic acids, and small molecules","year":2025,"lang":"en","type":"article","venue":"Briefings in Bioinformatics","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Small molecule; Nucleic acid; Binding site; Cysteine; Ligand (biochemistry); Protein–protein interaction; Python (programming language); Peptide; Plasma protein binding","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":[0.0005784627,0.001011765,0.0008491769,0.001394402,0.0002652508,0.0007883921,0.0005641028,0.000836172,0.001259738],"category_scores_gemma":[0.000921734,0.00015365,0.0005934315,0.0005732625,0.000229298,0.0003273552,0.0002445243,0.0004619362,0.0007629719],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004693036,"about_ca_system_score_gemma":0.0005042463,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002608735,"about_ca_topic_score_gemma":0.00238714,"domain_scores_codex":[0.9997198,0.0000604679,0.0000149405,0.00008943585,0.00007426955,0.00004108503],"domain_scores_gemma":[0.9995302,0.0002075533,0.00007370227,0.00002822774,0.0001148905,0.00004541427],"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":[0.001241099,0.0005745342,0.06625518,0.0008563405,0.0003477739,0.0002884151,0.00004161061,0.5345228,0.1426692,0.001156214,0.009122816,0.2429241],"study_design_scores_gemma":[0.00001645424,0.000180485,0.00874767,0.00002620413,0.00003593093,0.00009123611,0.0000198308,0.9620974,0.02644803,0.000890688,0.001432735,0.00001339155],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8615631,0.003521122,0.1255131,0.0002281566,0.00005655664,0.0001237739,0.003874911,0.00241367,0.002705533],"genre_scores_gemma":[0.935578,0.0005451895,0.0568172,0.00008633821,0.00003392474,0.00008599341,0.005326039,0.00008062162,0.001446703],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002608735,"threshold_uncertainty_score":0.005187035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01137480711774124,"score_gpt":0.2391244631498523,"score_spread":0.2277496560321111,"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."}}