{"id":"W2035503247","doi":"10.1021/ac026196y","title":"Quantitative Chemical Proteomics for Identifying Candidate Drug Targets","year":2003,"lang":"en","type":"article","venue":"Analytical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":190,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institute for Theoretical Astrophysics","keywords":"Chemistry; Proteomics; Surface plasmon resonance; Computational biology; Small molecule; Mass spectrometry; Drug discovery; Quantitative proteomics; Affinity chromatography; Chromatography; Multiplex; Chemical biology; Biochemistry; Combinatorial chemistry; Nanotechnology; Enzyme; Bioinformatics","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":[],"consensus_categories":[],"category_scores_codex":[0.000148708,0.0002325406,0.0002706216,0.00001415018,0.0001432782,0.00005107799,0.0002649611,0.0001714066,0.0005082341],"category_scores_gemma":[0.00031956,0.0002413385,0.0001821438,0.0001302047,0.0001365546,0.00008112633,0.00005312197,0.0003188495,0.00001571729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001278639,"about_ca_system_score_gemma":0.00007835928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009796721,"about_ca_topic_score_gemma":7.448521e-7,"domain_scores_codex":[0.9985561,0.000005237656,0.0003619189,0.0004989825,0.000158719,0.0004190149],"domain_scores_gemma":[0.9989989,0.0001384691,0.0001295505,0.0004193071,0.0001260775,0.0001877304],"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":[0.00004896832,0.0001027259,0.0001257269,0.0002936068,0.00005502031,0.000003426039,0.00003910653,0.00001311918,0.9732344,0.02416177,0.001830501,0.00009169197],"study_design_scores_gemma":[0.0003453999,0.00000386619,4.711183e-7,0.00003522201,0.00004736245,0.00001360929,0.00008292668,0.002773812,0.9575093,0.02798656,0.0109024,0.0002990649],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5912811,0.0003098802,0.3480334,0.0006928962,0.00003169388,0.0006897504,0.0002864047,0.0004902923,0.05818462],"genre_scores_gemma":[0.718206,0.00003577967,0.2739408,0.0001122444,0.0001247831,0.0007617053,0.0002136112,0.00007422832,0.006530846],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.126925,"threshold_uncertainty_score":0.9841498,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02674483617322587,"score_gpt":0.3305182218629255,"score_spread":0.3037733856896996,"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."}}