{"id":"W4241933606","doi":"10.26434/chemrxiv.14195207","title":"Strategy for Lead Identification for Understudied Kinases","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Cancer Treatment and Pharmacology","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novartis Pharma; Genentech; National Institutes of Health; Ministero dello Sviluppo Economico; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; Ontario Ministry of Economic Development and Innovation; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Gillings School of Public Health; North Carolina Biotechnology Center; Pfizer","keywords":"Kinase; Function (biology); Protein-Serine-Threonine Kinases; Pyrimidine; Identification (biology); Computational biology; Cancer research; Biology; Cell biology; Chemistry; Biochemistry; Protein kinase A; Botany","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":[0.001162783,0.0008787471,0.0009484171,0.0007670475,0.0006799207,0.001402489,0.0009954675,0.000883292,0.01216773],"category_scores_gemma":[0.001245636,0.0005199492,0.0005763565,0.0006608989,0.000672281,0.001140407,0.001595131,0.003084433,0.007966862],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006805129,"about_ca_system_score_gemma":0.001120719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004225099,"about_ca_topic_score_gemma":0.00121679,"domain_scores_codex":[0.9993263,0.0001044235,0.0000430563,0.0001533773,0.0002739171,0.00009899525],"domain_scores_gemma":[0.9995595,0.00008430516,0.00004942768,0.0001415185,0.0001043221,0.00006083704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001017736,0.0004678275,0.0005710829,0.001606637,0.0001624299,0.001134005,0.0004621162,0.005725815,0.6779913,0.06429647,0.04798767,0.1985768],"study_design_scores_gemma":[0.0003261385,0.001111977,0.0003100511,0.0001245791,0.00009492474,0.001258092,0.000131024,0.01200196,0.654785,0.01893713,0.3108211,0.00009801683],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1056878,0.01696782,0.7699715,0.007840874,0.002956914,0.002934904,0.005763131,0.008177362,0.07969964],"genre_scores_gemma":[0.5064228,0.02109285,0.3786361,0.006457231,0.0005478885,0.002914004,0.005296257,0.001872546,0.07676037],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01216773,"threshold_uncertainty_score":0.04070508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1826039178174123,"score_gpt":0.4232937937498312,"score_spread":0.2406898759324189,"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."}}