{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001141615,0.001945736,0.001372466,0.001035839,0.0006750459,0.001205106,0.003712493,0.001730812,0.0180219],"category_scores_gemma":[0.003941837,0.0005466195,0.001393736,0.001207625,0.0004523991,0.001231538,0.001872714,0.002051404,0.00797872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001059742,"about_ca_system_score_gemma":0.00179621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006671816,"about_ca_topic_score_gemma":0.01048496,"domain_scores_codex":[0.9994167,0.0001595469,0.00002804966,0.0001229491,0.0002148702,0.000057941],"domain_scores_gemma":[0.9993969,0.0002289087,0.00004459828,0.0001157176,0.0001487757,0.00006507032],"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":[0.0007000072,0.0004512964,0.006638595,0.00146778,0.0005959661,0.0002898292,0.0000876853,0.4170858,0.01235958,0.02779166,0.370189,0.1623428],"study_design_scores_gemma":[0.0001502095,0.0001286223,0.0008503451,0.00005251489,0.00004385997,0.00009656038,0.0000164257,0.958926,0.005246223,0.01639875,0.01802772,0.00006267588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0747885,0.002281837,0.7340847,0.001757537,0.0006987393,0.0006555878,0.05815373,0.1090957,0.01848375],"genre_scores_gemma":[0.3858513,0.001506863,0.451376,0.001184035,0.0001260606,0.001536606,0.1293502,0.0130341,0.01603487],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0180219,"threshold_uncertainty_score":0.06028932,"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."}}