{"id":"W3039347241","doi":"10.1021/acsmedchemlett.0c00279","title":"Mining Public Domain Data to Develop Selective DYRK1A Inhibitors","year":2020,"lang":"en","type":"article","venue":"ACS Medicinal Chemistry Letters","topic":"Ubiquitin and proteasome pathways","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Janssen Research and Development; Eshelman Institute for Innovation, University of North Carolina at Chapel Hill; Pharmaceuticals Bayer; Innovative Medicines Initiative; Novartis Pharma; Canada Foundation for Innovation; Ontario Ministry of Economic Development and Innovation; Wellcome Trust; Fundação de Amparo à Pesquisa do Estado de São Paulo; Genome Canada; Merck KGaA; AbbVie; Takeda Pharmaceuticals U.S.A.; Pfizer; Biotechnology and Biological Sciences Research Council; Boehringer Ingelheim","keywords":"Kinome; DYRK1A; GSK-3; Kinase; Cyclin-dependent kinase; Drug discovery; Computational biology; Cyclin-dependent kinase 9; Computer science; Biochemistry; Chemistry; Biology; Cyclin-dependent kinase 2; Protein kinase A; Cell cycle; Gene","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":[0.001525929,0.0006455389,0.000812545,0.003406041,0.0003950406,0.001333199,0.0007796379,0.0007371732,0.002308729],"category_scores_gemma":[0.005135463,0.0002334504,0.0007012846,0.003353876,0.0002373844,0.001102099,0.0006508724,0.0008102992,0.001708239],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007896586,"about_ca_system_score_gemma":0.001704928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003917215,"about_ca_topic_score_gemma":0.008665149,"domain_scores_codex":[0.9989086,0.0002258853,0.0001468447,0.0002557422,0.00035093,0.0001120391],"domain_scores_gemma":[0.9979937,0.0008116961,0.0004331883,0.000342456,0.0003154203,0.0001036341],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.004732806,0.002461109,0.1587133,0.009346247,0.002371581,0.003658314,0.0004403503,0.03811371,0.08534418,0.008620963,0.2744716,0.4117258],"study_design_scores_gemma":[0.001243903,0.001869429,0.123235,0.001256067,0.001724834,0.003931914,0.002416365,0.233777,0.1050322,0.02279487,0.5025232,0.0001952432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.3767534,0.01337414,0.02272275,0.005727933,0.0002703516,0.0005967445,0.5612276,0.006514452,0.01281255],"genre_scores_gemma":[0.3785427,0.003927675,0.03249919,0.0006213061,0.00006394467,0.0002883455,0.5820121,0.0001526863,0.001892044],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003917215,"threshold_uncertainty_score":0.008069932,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04213997391401919,"score_gpt":0.262182094261412,"score_spread":0.2200421203473928,"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."}}