{"id":"W4400317002","doi":"10.1038/s41597-024-03585-6","title":"Author Correction: PLAS-20k: Extended Dataset of Protein-Ligand Affinities from MD Simulations for Machine Learning Applications","year":2024,"lang":"en","type":"erratum","venue":"Scientific Data","topic":"Machine Learning in Bioinformatics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Affinities; Binding affinities; Ligand (biochemistry); Computer science; Computational biology; Artificial intelligence; Chemistry; Biology; Stereochemistry; Biochemistry; Receptor","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007447127,0.0003209331,0.0003229404,0.0002062172,0.0004824552,0.0004116248,0.001560402,0.000400792,0.0001262375],"category_scores_gemma":[0.0009951049,0.0003116549,0.0000921304,0.000308544,0.0003423769,0.00002645363,0.00144794,0.000681242,0.00006780475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002064301,"about_ca_system_score_gemma":0.0005037056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006827178,"about_ca_topic_score_gemma":0.0003865532,"domain_scores_codex":[0.9975558,0.00006997776,0.0006564376,0.001044162,0.000382443,0.0002912136],"domain_scores_gemma":[0.9963682,0.00009357405,0.0005100169,0.002728282,0.0002109485,0.00008899658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002654893,0.00005194771,0.000007308477,0.0003622511,0.0001333186,4.226762e-7,0.00006808028,0.0004002553,0.004226077,0.00006544673,0.9921429,0.0025154],"study_design_scores_gemma":[0.0001671656,0.00007710013,0.000004328528,0.0001482358,0.0001644819,0.000003408391,0.00008153218,0.09556891,0.001958736,0.0002175003,0.901321,0.0002876089],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0001663232,0.005584958,0.06727933,0.0002523505,0.03025111,0.002447688,0.8906103,0.0001188838,0.003288983],"genre_scores_gemma":[0.001291519,0.00004601384,0.006501336,0.00001967837,0.0009675696,0.0001262885,0.7630114,0.00005347111,0.2279827],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2246937,"threshold_uncertainty_score":0.9999335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03560600538244824,"score_gpt":0.3239491444120766,"score_spread":0.2883431390296284,"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."}}