{"id":"W4323361838","doi":"10.3390/ijms24065088","title":"Utilization of Supervised Machine Learning to Understand Kinase Inhibitor Toxophore Profiles","year":2023,"lang":"en","type":"article","venue":"International Journal of Molecular Sciences","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Common Fund; Division of Chemistry; Novartis Pharma; University of North Carolina at Chapel Hill; Ministero dello Sviluppo Economico; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Ministry of Economic Development and Innovation; Wellcome Trust; Itä-Suomen Yliopisto; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Pfizer; National Institutes of Health; National Science Foundation","keywords":"Kinome; Kinase; Computational biology; Bioinformatics; Medicine; Pharmacology; Computer science; Biology; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001419978,0.0007673681,0.0009950508,0.001827901,0.0002663595,0.000711593,0.0005654799,0.0005516812,0.001437246],"category_scores_gemma":[0.003486919,0.0002197612,0.0007929014,0.0007497118,0.0003454382,0.0009003652,0.0005124909,0.001030063,0.0004267549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006757018,"about_ca_system_score_gemma":0.0008648608,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001337958,"about_ca_topic_score_gemma":0.002026737,"domain_scores_codex":[0.9995815,0.0001169983,0.00003834263,0.00009959141,0.0001216102,0.0000419063],"domain_scores_gemma":[0.9979318,0.001090277,0.0003423669,0.0001964411,0.0003709379,0.00006809171],"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":[0.0009573378,0.001278639,0.02964107,0.0007569534,0.000329489,0.0002265822,0.0001141426,0.6270032,0.05200028,0.003761163,0.004987119,0.2789441],"study_design_scores_gemma":[0.00002752752,0.0003207301,0.002174869,0.000009887921,0.00002645496,0.00003881147,0.00001383618,0.9849642,0.009302446,0.002146145,0.0009583803,0.00001681941],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6191512,0.002070281,0.3590237,0.0007422926,0.0000797368,0.0005608347,0.00460057,0.006832603,0.006938679],"genre_scores_gemma":[0.9083294,0.0004562767,0.08468343,0.0002124579,0.00003620686,0.0002409231,0.00437989,0.0001333792,0.001528119],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001827901,"threshold_uncertainty_score":0.007509649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05616168767808376,"score_gpt":0.3494100325123018,"score_spread":0.2932483448342181,"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."}}