{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005828638,0.00337992,0.001871858,0.003012988,0.001601295,0.002944137,0.005836654,0.003487028,0.004692031],"category_scores_gemma":[0.01544036,0.0007014427,0.002101087,0.004492158,0.001279699,0.002634186,0.003234269,0.0026036,0.002818052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002443782,"about_ca_system_score_gemma":0.001970684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01672011,"about_ca_topic_score_gemma":0.01728341,"domain_scores_codex":[0.9942672,0.001948646,0.0003379054,0.0009861265,0.001897405,0.0005627708],"domain_scores_gemma":[0.9908732,0.004033335,0.0003826619,0.002103513,0.002013394,0.0005938418],"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":[0.001965355,0.002550675,0.02214983,0.002526874,0.0007513413,0.0008869832,0.0001998399,0.4669915,0.004929481,0.0197216,0.2854183,0.1919083],"study_design_scores_gemma":[0.0007741348,0.001160535,0.01098214,0.0003340692,0.0001211973,0.0006629421,0.0004215016,0.8343285,0.01094901,0.01975624,0.1204178,0.00009190662],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6123969,0.02244242,0.0943548,0.008221507,0.002192384,0.001632019,0.1503156,0.02929014,0.07915425],"genre_scores_gemma":[0.426249,0.003330548,0.1513527,0.001434645,0.0002830652,0.0008405242,0.4065481,0.001706052,0.00825543],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01672011,"threshold_uncertainty_score":0.03324556,"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."}}