{"id":"W4415054797","doi":"10.1101/2025.10.09.681452","title":"Cellular Context Influences Kinase Inhibitor Selectivity","year":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eshelman Institute for Innovation, University of North Carolina at Chapel Hill; Gillings School of Public Health; Ontario Genomics; National Institutes of Health; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Genome Canada; Fundação de Amparo à Pesquisa do Estado de São Paulo; McGill University; Genentech; Bayer; Pfizer","keywords":"Kinase; Selectivity; Context (archaeology); Protein kinase A; Drug discovery; Protein kinase inhibitor; Drug development","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":[0.0004408481,0.0003843199,0.0006984097,0.0002640625,0.0003702295,0.001882887,0.0003928821,0.0004182632,0.003438677],"category_scores_gemma":[0.001518775,0.0002769423,0.0002572724,0.0004573726,0.0004272319,0.0008324613,0.0006805725,0.0008241424,0.0009090491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006454099,"about_ca_system_score_gemma":0.0004433704,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001284492,"about_ca_topic_score_gemma":0.002976171,"domain_scores_codex":[0.9994372,0.0001243113,0.00003762231,0.0001661083,0.0001255685,0.0001092195],"domain_scores_gemma":[0.9994363,0.0003140761,0.0000695541,0.00004986819,0.0000750536,0.0000550634],"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":[0.0004580734,0.00008260593,0.005137579,0.0003925724,0.00004328635,0.0002514065,0.00009507277,0.004433523,0.9738782,0.001805631,0.0005706322,0.01285136],"study_design_scores_gemma":[0.00004786187,0.0006044302,0.02403417,0.00005077548,0.0001927554,0.0005847855,0.0005457523,0.01743637,0.9392563,0.0024015,0.01478285,0.00006233394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9706761,0.004837442,0.009787244,0.0004217502,0.00009902711,0.00003675489,0.001019177,0.0001718251,0.01295072],"genre_scores_gemma":[0.994493,0.002006376,0.002146727,0.0001392358,0.00001292287,0.00002202432,0.0004493087,0.0000662324,0.0006641353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003438677,"threshold_uncertainty_score":0.01150352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01679648921457265,"score_gpt":0.2526783169193745,"score_spread":0.2358818277048019,"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."}}