{"id":"W4402940667","doi":"10.1021/acs.jmedchem.4c01463","title":"Multiplexed Target Profiling with Integrated Chemical Genomics and Chemical Proteomics","year":2024,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Innovation and Technology Fund; Research Grants Council, University Grants Committee; National Natural Science Foundation of China","keywords":"Chemistry; Profiling (computer programming); Proteomics; Genomics; Computational biology; Biochemistry; Genome; Gene; Computer science; Biology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007399981,0.0006940174,0.0007854279,0.0009129733,0.0002050225,0.0006402712,0.0006439629,0.0005576964,0.0007171087],"category_scores_gemma":[0.0005448117,0.000442451,0.0004527871,0.0007332779,0.000421226,0.0009371422,0.0009996174,0.0009922902,0.0003051476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004470801,"about_ca_system_score_gemma":0.0004849066,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004681988,"about_ca_topic_score_gemma":0.0008905809,"domain_scores_codex":[0.9993223,0.0001131527,0.00002745503,0.0002051209,0.0002694197,0.00006254356],"domain_scores_gemma":[0.9996859,0.0001191664,0.00006488121,0.00004982368,0.00005325701,0.00002703269],"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":[0.0001476433,0.00007927039,0.0007666132,0.0001200445,0.0000499185,0.0000546295,0.00001960194,0.002330038,0.9687991,0.0009653514,0.0001835747,0.02648436],"study_design_scores_gemma":[0.0000386483,0.0003730725,0.002724617,0.000006749984,0.00005435174,0.0002674679,0.00002933089,0.05193779,0.9393174,0.001759708,0.003441778,0.00004921086],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3795128,0.003340127,0.6093316,0.0005383163,0.0001326965,0.0004560117,0.001320156,0.00230054,0.003067811],"genre_scores_gemma":[0.614755,0.00200646,0.3798764,0.0003837213,0.0001190502,0.000421272,0.0008048817,0.00009906253,0.001534102],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0009129733,"threshold_uncertainty_score":0.003913522,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01054232948010828,"score_gpt":0.2630515055654022,"score_spread":0.2525091760852939,"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."}}