{"id":"W4388020043","doi":"10.1038/s41589-023-01459-3","title":"Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors","year":2023,"lang":"en","type":"article","venue":"Nature Chemical Biology","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":82,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Elitenetzwerk Bayern; Genentech; Deutsche Forschungsgemeinschaft; European Commission; Ontario Genomics Institute; European Federation of Pharmaceutical Industries and Associations; Merck KGaA; Ontario Genomics; Genome Canada; Bundesministerium für Bildung und Forschung; Diamond Light Source; McGill University; Bayer; Pfizer; Deutsches Krebsforschungszentrum; Bristol-Myers Squibb","keywords":"Kinome; Chemical biology; Drug discovery; Phosphoproteomics; Kinase; Computational biology; Proteomics; Syk; Biology; Tyrosine kinase; Chemistry; Biochemistry; Protein kinase A; Signal transduction; Protein phosphorylation; Gene","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001601455,0.0002050133,0.0002965814,0.0000252618,0.0000600581,0.000008863581,0.0005696288,0.001091688,0.0006408897],"category_scores_gemma":[0.0005791569,0.0001449563,0.0001600617,0.0004325502,0.0003095948,0.00001839195,0.0002313171,0.001098164,0.00003564283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002893196,"about_ca_system_score_gemma":0.00003331983,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003156893,"about_ca_topic_score_gemma":1.66761e-7,"domain_scores_codex":[0.9987057,0.00001406564,0.0003748157,0.0004182302,0.0001260024,0.0003612253],"domain_scores_gemma":[0.9987212,0.000398326,0.0001564139,0.0005408654,0.00008164648,0.000101504],"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":[0.00003736205,0.00005972653,0.002147621,0.0000824801,0.00002731544,0.000001837335,0.00002977037,7.32249e-7,0.9757425,0.0008364705,0.02076357,0.0002706107],"study_design_scores_gemma":[0.0002898766,0.000004011464,0.00002234308,0.00002453477,0.00001659097,0.00001062945,0.00004362419,0.00004045542,0.9617286,0.003507917,0.03415168,0.0001597537],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9907069,0.0003083899,0.000004324088,0.002388013,0.00006243082,0.0001205434,0.0004717749,0.0001581353,0.005779534],"genre_scores_gemma":[0.9969186,0.00003700489,0.0004099403,0.0002163016,0.0006573494,0.0001254318,0.001238856,0.00002213157,0.0003743825],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01401392,"threshold_uncertainty_score":0.84201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008417779643692579,"score_gpt":0.2661253709367342,"score_spread":0.2577075912930417,"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."}}