{"id":"W4408660256","doi":"10.1021/acs.jmedchem.5c00097","title":"Chemoproteomic Profiling of <i>C. albicans</i> for Characterization of Antifungal Kinase Inhibitors","year":2025,"lang":"en","type":"article","venue":"Journal of Medicinal Chemistry","topic":"Antifungal resistance and susceptibility","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Structural Genomics Consortium","funders":"National Institute of General Medical Sciences; National Cancer Institute; Genentech; Janssen Biotech; Innovative Medicines Initiative; National Institutes of Health; Structural Genomics Consortium; Merck KGaA; Takeda Foundation; Ontario Genomics Institute; Genome Canada; North Carolina Biotechnology Center; National Institute of Allergy and Infectious Diseases; Bayer; Pfizer; Bristol-Myers Squibb; Boehringer Ingelheim","keywords":"Chemistry; Antifungal; Profiling (computer programming); Candida albicans; Kinase; Computational biology; Combinatorial chemistry; Stereochemistry; Biochemistry; Microbiology; Computer science; Biology; Programming language","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001514361,0.0004301479,0.0002790858,0.000630549,0.0002255178,0.0003460281,0.0002158387,0.0002693592,0.001888363],"category_scores_gemma":[0.0001870875,0.0001251288,0.0002484302,0.0005198157,0.0001052768,0.0002186084,0.0001814159,0.0003509712,0.000629072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002004988,"about_ca_system_score_gemma":0.0002511923,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001055003,"about_ca_topic_score_gemma":0.001846601,"domain_scores_codex":[0.9998955,0.000008432783,0.000006545087,0.00002078164,0.00004670647,0.00002202923],"domain_scores_gemma":[0.999922,0.00001184836,0.00001305358,0.000005019019,0.00003222627,0.00001575945],"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":[0.00007786603,0.00001593732,0.0003055468,0.00003595944,0.000005248096,0.0000237773,0.000006285367,0.00006075084,0.9973686,0.0000262277,0.0001467663,0.001927095],"study_design_scores_gemma":[0.000009832337,0.0001884292,0.0158937,0.00001379141,0.00002459835,0.0002682454,0.00004526219,0.001920716,0.9780663,0.0000497033,0.003507058,0.0000123402],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.963348,0.004043709,0.01310142,0.0003851337,0.00005940257,0.0002700325,0.01101336,0.0005403348,0.007238568],"genre_scores_gemma":[0.9456214,0.003876804,0.03080563,0.000510595,0.00002863892,0.0002641592,0.01227213,0.0001637069,0.006456949],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001888363,"threshold_uncertainty_score":0.006317198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008614289678778309,"score_gpt":0.2818587756968201,"score_spread":0.2732444860180418,"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."}}