{"id":"W4380867105","doi":"10.1093/bioadv/vbad072","title":"GDockScore: a graph-based protein–protein docking scoring function","year":2023,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Bioinformatics and Genomic Networks","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Docking (animal); Computer science; Macromolecular docking; Graph; Protein–ligand docking; Artificial intelligence; Machine learning; Computational biology; Virtual screening; Theoretical computer science; Protein structure; Bioinformatics; Drug discovery; Biology; Biochemistry","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":[0.000363326,0.000268043,0.0001989172,0.0001760661,0.000264516,0.0001064923,0.0002792465,0.0001659479,0.00001484913],"category_scores_gemma":[0.00005592763,0.0002451797,0.0001434139,0.0004712301,0.00009271537,0.00004542662,0.0001439165,0.0001520753,0.0001361958],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001971235,"about_ca_system_score_gemma":0.00008931897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004188146,"about_ca_topic_score_gemma":0.00001647445,"domain_scores_codex":[0.9984353,0.0000200794,0.0005394026,0.0002226426,0.0002678978,0.0005147001],"domain_scores_gemma":[0.9990362,0.00001213569,0.0002819919,0.0004586731,0.00008877584,0.0001222292],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001137916,0.0002441541,0.002995202,0.003218307,0.0004937883,0.00002116962,0.0008148765,0.09871135,0.2509147,0.009083945,0.0125431,0.6198215],"study_design_scores_gemma":[0.004404766,0.002184614,0.001541864,0.0009808973,0.0001016393,0.00002588982,0.001769994,0.2857419,0.1583259,0.01388297,0.528308,0.00273158],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7929319,0.001603966,0.1932906,0.0002585963,0.0009797278,0.001696265,0.00006644515,0.0003891674,0.008783329],"genre_scores_gemma":[0.9818309,0.0001619029,0.01601361,0.0003793704,0.0003796247,0.0001892909,0.0004360381,0.00004440857,0.0005648566],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6170899,"threshold_uncertainty_score":0.9998137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00998602961376684,"score_gpt":0.2305397237886313,"score_spread":0.2205536941748644,"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."}}