{"id":"W4312212332","doi":"10.1101/2022.12.02.518908","title":"GDockScore: a graph-based protein-protein docking scoring function","year":2022,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Docking (animal); Computer science; Macromolecular docking; Protein–ligand docking; Graph; DOCK; Artificial intelligence; Protein Data Bank (RCSB PDB); Machine learning; Computational biology; Theoretical computer science; Virtual screening; Bioinformatics; Drug discovery; Biology; Biochemistry","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":[0.001198671,0.001500392,0.001228524,0.001174713,0.0005440266,0.001108721,0.002785813,0.001435834,0.00719884],"category_scores_gemma":[0.003383845,0.0004207813,0.001191545,0.001277223,0.0005164751,0.0009503437,0.001709249,0.001690737,0.002902863],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001110931,"about_ca_system_score_gemma":0.001600968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006397356,"about_ca_topic_score_gemma":0.007590122,"domain_scores_codex":[0.9993679,0.0002114586,0.0000273391,0.0001094141,0.0002277777,0.00005616936],"domain_scores_gemma":[0.9993975,0.0002339989,0.00004728595,0.0001142981,0.0001404077,0.00006639807],"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":[0.0006268029,0.0003741747,0.005362206,0.0007239959,0.0004769083,0.0002014031,0.00004878635,0.685891,0.0101236,0.02863388,0.117303,0.1502341],"study_design_scores_gemma":[0.00007601694,0.00007393689,0.000400479,0.00001890586,0.00001963631,0.00004039918,0.000007752648,0.9821848,0.002523921,0.01024139,0.004387605,0.00002507947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.09480844,0.001819732,0.8333331,0.001420307,0.0004378837,0.0006298635,0.01555617,0.03940142,0.01259303],"genre_scores_gemma":[0.5423316,0.001033436,0.4122997,0.0009071713,0.00009960943,0.001063795,0.02861432,0.004005914,0.009644466],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00719884,"threshold_uncertainty_score":0.02408254,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02152161183274379,"score_gpt":0.2466471312837614,"score_spread":0.2251255194510176,"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."}}