{"id":"W1996808075","doi":"10.1021/ci900358z","title":"Quantum Chemical Associations Ligand−Residue: Their Role to Predict Flavonoid Binding Sites in Proteins","year":2010,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Quantum chemical; Ligand (biochemistry); Virtual screening; Flavonoid; Chemistry; Binding site; In silico; Residue (chemistry); Computational chemistry; Stereochemistry; Amino acid residue; Computational biology; Biochemistry; Molecular dynamics; Biology; Molecule; Organic chemistry; Peptide sequence; Receptor","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":[0.0008479507,0.0001027403,0.0001891825,0.0002749922,0.00004474548,0.0002253948,0.0003040613,0.00009126584,0.000002076219],"category_scores_gemma":[0.0008158716,0.00008791586,0.00006494496,0.0002892372,0.00001356752,0.002027608,0.0001454259,0.0004505534,0.000003696507],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006267815,"about_ca_system_score_gemma":0.0001397836,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000690422,"about_ca_topic_score_gemma":0.000001369291,"domain_scores_codex":[0.9987233,0.00002570871,0.0006728033,0.00009046406,0.0003179839,0.0001697762],"domain_scores_gemma":[0.9990385,0.0002111472,0.0002491801,0.0001077442,0.0002402779,0.0001531066],"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":[0.00003615288,0.00007532189,0.0005356589,0.00002465958,0.00002201226,0.000001118613,0.004421643,0.1384573,0.8373908,0.007448789,0.0001174985,0.011469],"study_design_scores_gemma":[0.0002915366,0.00001751825,0.0001057297,0.00003455643,0.00000288082,0.00002156132,0.00008034924,0.8748251,0.1197793,0.004697275,0.00005232594,0.00009194754],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7208075,0.00001140345,0.2781563,0.0007388436,0.0001067232,0.00008046704,0.000004836992,0.00001523164,0.00007866974],"genre_scores_gemma":[0.935266,0.000003702555,0.06443984,0.0001713667,0.0001018114,0.000004563815,0.000007849581,0.000003678934,0.000001186796],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7363678,"threshold_uncertainty_score":0.3585105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02264821486461149,"score_gpt":0.285543074953768,"score_spread":0.2628948600891565,"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."}}