{"id":"W3000417149","doi":"10.1101/2020.01.08.883009","title":"In depth analysis of kinase cross screening data to identify CAMKK2 inhibitory scaffolds","year":2020,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novartis Pharma; National Cancer Institute; National Institutes of Health; Ministero dello Sviluppo Economico; Medical Research Council; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Ministry of Economic Development and Innovation; Genome Canada; National Health and Medical Research Council; Pfizer","keywords":"AMPK; Protein kinase A; Kinase; Biology; Cell biology","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","open_science"],"consensus_categories":["open_science"],"category_scores_codex":[0.002581479,0.0006118331,0.001193156,0.001891869,0.0001097838,0.000739747,0.005600112,0.0003613646,0.00001467958],"category_scores_gemma":[0.0009060772,0.0007523654,0.00030381,0.006458463,0.0001212131,0.001207615,0.01036901,0.0005938098,0.00003018753],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002945066,"about_ca_system_score_gemma":0.001076945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004178386,"about_ca_topic_score_gemma":0.00004573015,"domain_scores_codex":[0.9937941,0.0006233997,0.001255635,0.00256164,0.001138485,0.0006266932],"domain_scores_gemma":[0.993332,0.0004299411,0.0007053721,0.004472035,0.0005934467,0.0004671979],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001188971,0.0005875598,0.08826899,0.00115183,0.002789798,0.0007790984,0.0002505472,0.4451932,0.4532719,0.006168294,0.001353392,0.00006650238],"study_design_scores_gemma":[0.0003431158,0.00002607933,0.6625582,0.0003131586,0.000304085,1.024956e-8,0.000002325783,0.2871862,0.04805462,0.00001316393,0.0004046661,0.0007943933],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5747004,0.0003270102,0.4225931,0.0004410072,0.000863219,0.0004455577,0.0004433503,0.0001826987,0.000003731697],"genre_scores_gemma":[0.8025525,0.00001643415,0.1967548,0.0003605316,0.0001974277,0.00005606311,0.000002716839,0.00005847202,0.000001098781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5742892,"threshold_uncertainty_score":0.9997801,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06854753171764603,"score_gpt":0.3500798500561464,"score_spread":0.2815323183385003,"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."}}