{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001229193,0.001665383,0.0008093933,0.0009174856,0.0004257618,0.001056216,0.002694118,0.00176268,0.008352508],"category_scores_gemma":[0.006382161,0.0004835249,0.000686717,0.0009832507,0.000567034,0.001606062,0.001133643,0.002086199,0.002749251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001534278,"about_ca_system_score_gemma":0.001654402,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01425929,"about_ca_topic_score_gemma":0.02052915,"domain_scores_codex":[0.9992968,0.000183691,0.00003831592,0.0001645448,0.0002342396,0.00008236923],"domain_scores_gemma":[0.998909,0.0004883473,0.00008854146,0.0001714574,0.0002489808,0.00009362354],"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":[0.0006739571,0.0002463003,0.006593407,0.0006487624,0.0003014453,0.000198309,0.00005616514,0.6631001,0.004588249,0.01445543,0.1111947,0.1979432],"study_design_scores_gemma":[0.00004297314,0.00004357621,0.0003913044,0.00001923385,0.00001136203,0.00002692041,0.000007905873,0.9880625,0.002026028,0.006533285,0.002820797,0.00001404206],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1114232,0.004110133,0.7762486,0.002269149,0.0007346517,0.0004244584,0.01733358,0.06897693,0.01847936],"genre_scores_gemma":[0.6031444,0.001082379,0.3444167,0.00136195,0.0001485967,0.0006517606,0.02881813,0.003421811,0.01695432],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01425929,"threshold_uncertainty_score":0.02835256,"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."}}