{"id":"W4403680155","doi":"10.1093/bioinformatics/btae636","title":"EuDockScore: Euclidean graph neural networks for scoring protein–protein interfaces","year":2024,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Monoclonal and Polyclonal Antibodies Research","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Research Chairs; University of Toronto; University of New Brunswick","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Computer science; Macromolecular docking; Docking (animal); Artificial intelligence; Machine learning; Artificial neural network; Graph; Protein–protein interaction; Computational biology; Protein structure; Theoretical computer science; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003019985,0.0002157877,0.0002769145,0.000194919,0.0001404335,0.0001762592,0.0001693449,0.0001015312,0.00007883073],"category_scores_gemma":[0.00008243514,0.0001538927,0.0001967041,0.0003057153,0.0001129103,0.0002586108,0.0001174217,0.0003635415,0.00008221998],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004135935,"about_ca_system_score_gemma":0.00007676075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003210052,"about_ca_topic_score_gemma":0.000006982388,"domain_scores_codex":[0.9984901,0.00001480711,0.0004788584,0.0001739774,0.0003413295,0.0005009421],"domain_scores_gemma":[0.9993317,0.00008912616,0.00005679548,0.0002229897,0.00009863728,0.0002007495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003003071,0.0004579015,0.00227874,0.02580308,0.00123994,0.000361658,0.004203327,0.001076265,0.01861439,0.03557979,0.04301929,0.8643625],"study_design_scores_gemma":[0.0007340692,0.001389436,0.0003286632,0.001743505,0.000072006,0.0001444628,0.000343554,0.932017,0.009923775,0.0009198395,0.0520094,0.0003742883],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9523304,0.003336807,0.03077116,0.00347654,0.0007304283,0.003124488,0.00007402237,0.0004173206,0.005738851],"genre_scores_gemma":[0.9740282,0.00009699019,0.01602207,0.0003294097,0.00071887,0.000194326,0.00008289282,0.00004672803,0.008480495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9309407,"threshold_uncertainty_score":0.6275563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03702823887180946,"score_gpt":0.3170168609949492,"score_spread":0.2799886221231398,"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."}}