{"id":"W2423377342","doi":"10.1002/prot.25063","title":"A benchmark testing ground for integrating homology modeling and protein docking","year":2016,"lang":"en","type":"article","venue":"Proteins Structure Function and Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences","keywords":"Docking (animal); Homology modeling; Protein–ligand docking; Protein Data Bank (RCSB PDB); Macromolecular docking; Computer science; Threading (protein sequence); Protein structure; Computational biology; Protein Data Bank; Protein structure prediction; Searching the conformational space for docking; Homology (biology); Biological system; Chemistry; Biology; Bioinformatics; Amino acid; Biochemistry; Drug discovery; Virtual screening; Enzyme","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000171908,0.0002410322,0.0001857849,0.00007564367,0.0002862048,0.00007948502,0.00008570324,0.0002462794,0.000005205825],"category_scores_gemma":[0.0003920207,0.0001562038,0.00004232713,0.00008659617,0.0000986181,0.00003524472,0.0001215819,0.0001103475,3.317714e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001717884,"about_ca_system_score_gemma":0.00005396692,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001422957,"about_ca_topic_score_gemma":0.00004139796,"domain_scores_codex":[0.9989541,0.00002327684,0.0003483617,0.0002753203,0.0001031583,0.000295744],"domain_scores_gemma":[0.9993612,0.00002638125,0.0001707364,0.0001942529,0.0001576234,0.00008981436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003160592,0.00001131714,0.001400482,0.0004320243,0.00008901408,6.169174e-7,0.0001771001,0.00008686023,0.7113608,0.005120474,0.00003536556,0.2809699],"study_design_scores_gemma":[0.01353611,0.009067639,0.003428677,0.001531266,0.0004080997,0.001269337,0.002258683,0.632859,0.1102405,0.2058078,0.01548322,0.004109692],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5066981,0.0002046,0.4917973,0.0001054143,0.00009765696,0.0008311417,0.00004585771,0.00002884658,0.0001910657],"genre_scores_gemma":[0.8668476,0.00001762961,0.1325167,0.0001719565,0.000221783,0.00008410289,0.00005382683,0.00001938132,0.00006699657],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6327721,"threshold_uncertainty_score":0.6369806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0103227635465813,"score_gpt":0.2183435776886768,"score_spread":0.2080208141420955,"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."}}