{"id":"W3137511353","doi":"10.26434/chemrxiv.14195207.v1","title":"Strategy for Lead Identification for Understudied Kinases","year":2021,"lang":"en","type":"preprint","venue":"ChemRxiv","topic":"Histone Deacetylase Inhibitors Research","field":"Biochemistry, Genetics and Molecular Biology","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; Computational biology; Cancer research; Cell biology; Chemistry; Biology; Biochemistry; Protein kinase A","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":[0.001070894,0.0007736621,0.0008851843,0.0006568196,0.0005791735,0.001057031,0.0008897315,0.0008301582,0.006886728],"category_scores_gemma":[0.0009255163,0.0004454775,0.0005358209,0.0005813481,0.0005580934,0.0009086668,0.001325706,0.002567475,0.004536713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005861978,"about_ca_system_score_gemma":0.0009783491,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003124948,"about_ca_topic_score_gemma":0.0009350005,"domain_scores_codex":[0.9993543,0.000105628,0.00004847359,0.0001595234,0.0002285734,0.0001034268],"domain_scores_gemma":[0.9996403,0.00007333163,0.00004745956,0.0001129151,0.00007826864,0.00004772504],"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":[0.0009049933,0.0004093364,0.0004343156,0.001269527,0.0001318391,0.0009627811,0.0004050487,0.004100817,0.8265398,0.03415984,0.01159891,0.1190829],"study_design_scores_gemma":[0.0002897142,0.001363338,0.0002814647,0.00009681184,0.0001132555,0.001153683,0.0001028238,0.006565702,0.8117867,0.01099807,0.167161,0.0000874617],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1548356,0.01798011,0.7700832,0.003789006,0.001880888,0.002900847,0.003230903,0.003801179,0.0414981],"genre_scores_gemma":[0.5648366,0.02298715,0.3536666,0.003417265,0.0003388717,0.002489226,0.00334078,0.0009638484,0.04795968],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006886728,"threshold_uncertainty_score":0.02303839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0784729463957375,"score_gpt":0.3620544759722936,"score_spread":0.2835815295765561,"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."}}