{"id":"W2793083756","doi":"10.1016/j.ijbiomac.2018.02.026","title":"Inhibitory effect of pyrogallol on α-glucosidase: Integrating docking simulations with inhibition kinetics","year":2018,"lang":"en","type":"article","venue":"International Journal of Biological Macromolecules","topic":"Natural Antidiabetic Agents Studies","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Research Foundation of Korea; Ministry of Health, British Columbia; Korea Health Industry Development Institute; Ministry of Science, ICT and Future Planning","keywords":"Pyrogallol; Docking (animal); Kinetics; Chemistry; Enzyme inhibition; Inhibitory postsynaptic potential; Biochemistry; Stereochemistry; Biophysics; Enzyme; Biology; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001926451,0.0001570205,0.0003333805,0.0001845469,0.00004569909,0.0000170674,0.0001239734,0.00008296317,0.00006814664],"category_scores_gemma":[0.0007557053,0.00008319768,0.0001322631,0.0001271026,0.0003248953,0.00005302653,0.00006697424,0.0002576986,0.000006089556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007981417,"about_ca_system_score_gemma":0.00002393207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000369819,"about_ca_topic_score_gemma":0.000002874514,"domain_scores_codex":[0.9986747,0.00009452955,0.000475548,0.0001394737,0.0004802258,0.0001355136],"domain_scores_gemma":[0.9981228,0.0003586269,0.000437778,0.00007480451,0.0009410442,0.00006492931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00257429,0.0003689046,0.0429545,0.00002814542,0.00047804,0.0003355076,0.0001390587,0.00007275266,0.9416557,0.0005682352,0.0001390684,0.01068578],"study_design_scores_gemma":[0.002488322,0.01328246,0.0269247,0.001277646,0.00009508868,0.000363302,0.00009064535,0.0001706045,0.9546062,0.0003040741,0.0002693223,0.000127571],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9969282,0.0001241751,0.0009569351,0.0008139141,0.0002888447,0.0001428829,0.00001548558,0.00001407721,0.000715457],"genre_scores_gemma":[0.9974353,0.00002749185,0.001683441,0.0002558106,0.0005623263,0.000001548066,0.00001385206,0.000009385548,0.00001082308],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01602979,"threshold_uncertainty_score":0.3392703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01495805795523016,"score_gpt":0.3033944308647203,"score_spread":0.2884363729094902,"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."}}