{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003191979,0.0002498897,0.0002069779,0.00002028506,0.0001341604,0.00003813052,0.000804989,0.0001461869,0.00005284675],"category_scores_gemma":[0.001089372,0.0002497269,0.00003317059,0.0004143082,0.0001025782,0.00001062541,0.0006091,0.0002329962,0.00003010925],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003275985,"about_ca_system_score_gemma":0.000196988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007424505,"about_ca_topic_score_gemma":0.000004316483,"domain_scores_codex":[0.998155,0.00004975381,0.0002741818,0.0007562534,0.0003142903,0.0004505513],"domain_scores_gemma":[0.9987203,0.00002469425,0.00009278022,0.0005466941,0.0001511519,0.0004644033],"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":[0.00006868277,0.0000131492,0.0005610258,0.00005413817,0.00005593305,0.00003263655,0.00046883,0.000002301105,0.9056385,0.000001752442,0.091993,0.001110086],"study_design_scores_gemma":[0.0004550525,0.00007466107,0.00009020897,0.00003114405,0.00001596234,0.0000445878,0.0006068408,0.000008232918,0.8385223,0.00000262305,0.1598552,0.0002931712],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9537433,0.000199299,0.001362154,0.04225115,0.00009699603,0.000189428,0.0000509148,0.00004366404,0.002063137],"genre_scores_gemma":[0.9685149,0.00001109337,0.003258949,0.02543275,0.001818528,0.00002980685,0.0008507626,0.00003787928,0.00004535437],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06786219,"threshold_uncertainty_score":0.9999955,"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."}}