{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01054649,0.002650065,0.002658074,0.004538199,0.003944627,0.005740501,0.004551074,0.004218261,0.1648825],"category_scores_gemma":[0.1482373,0.001668968,0.001982406,0.005395102,0.002238294,0.003245132,0.005427342,0.01025167,0.12902],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003325511,"about_ca_system_score_gemma":0.007052564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008690524,"about_ca_topic_score_gemma":0.01138628,"domain_scores_codex":[0.9872245,0.001782392,0.002455519,0.002197358,0.00581961,0.0005206866],"domain_scores_gemma":[0.9032636,0.02571677,0.004298662,0.01168113,0.05268492,0.002354922],"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":[0.00004035083,0.000005645135,0.0001179203,0.0001680961,0.00001619764,0.00008863998,0.00003334257,0.0001248101,0.0001145165,0.0007985096,0.9935051,0.004986822],"study_design_scores_gemma":[0.00006219706,0.00001395295,0.0006252991,0.0003328837,0.0000521314,0.0004226211,0.00005978907,0.0006340345,0.001225637,0.00370295,0.9928047,0.00006381299],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0009940612,0.001236714,0.02152786,0.04238935,0.8449912,0.0001198078,0.06867972,0.01188682,0.00817441],"genre_scores_gemma":[0.04775562,0.006522836,0.1038878,0.07532904,0.09230309,0.001819641,0.2208332,0.07126836,0.3802803],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.1648825,"threshold_uncertainty_score":0.5515869,"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."}}