{"id":"W4387466372","doi":"10.1039/d3dd00110e","title":"Machine learning-augmented docking. 1. CYP inhibition prediction","year":2023,"lang":"en","type":"article","venue":"Digital Discovery","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Docking (animal); Computer science; Computational biology; Chemistry; Artificial intelligence; Machine learning; Biology; Medicine","routes":{"ca_aff":true,"ca_fund":true,"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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.000300291,0.0001949254,0.0001645424,0.0003085877,0.0001682624,0.001279878,0.00032806,0.00005474179,0.000007534647],"category_scores_gemma":[0.0002574635,0.0001921166,0.0001397518,0.001252834,0.00004841443,0.005465077,0.0005247692,0.0001978363,0.0004269676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009630145,"about_ca_system_score_gemma":0.00007642562,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001239094,"about_ca_topic_score_gemma":0.000001625096,"domain_scores_codex":[0.9981734,0.00009536805,0.0003110241,0.0005156808,0.0005653403,0.0003392111],"domain_scores_gemma":[0.999137,0.0002831853,0.0001083624,0.0003195641,0.00006358619,0.00008823048],"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":[0.0001200292,0.0005785578,0.02791334,0.0001375155,0.000197407,0.0002347505,0.001367623,0.5596213,0.002435162,0.2204591,0.0118465,0.1750887],"study_design_scores_gemma":[0.0006290941,0.0001723515,0.0351408,0.0000748883,0.000008058725,0.00003948919,0.00005654176,0.9153887,0.001146766,0.03362336,0.01334023,0.0003797346],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4183919,0.0000825241,0.5645213,0.0006907758,0.00119882,0.0002578588,0.0002220503,0.001599341,0.01303551],"genre_scores_gemma":[0.9963689,0.00002571113,0.0005412176,0.000128105,0.0001667354,0.00002687092,0.0007888349,0.00002326823,0.001930328],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5779771,"threshold_uncertainty_score":0.9997569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01752727975080155,"score_gpt":0.2584158889483695,"score_spread":0.2408886091975679,"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."}}