{"id":"W4231443012","doi":"10.1016/j.bpj.2013.11.1547","title":"Covalent Docking of Large Libraries for the Discovery of Chemical Probes","year":2014,"lang":"en","type":"article","venue":"Biophysical Journal","topic":"Click Chemistry and Applications","field":"Chemistry","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Covalent bond; Virtual screening; Chemistry; Docking (animal); Cysteine; Small molecule; Chemical library; Serine; Combinatorial chemistry; Drug discovery; Chemical biology; Electrophile; Computational biology; Biochemistry; Phosphorylation; Biology; Catalysis; Enzyme; Organic chemistry","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":[0.0007124215,0.0007997559,0.0007062252,0.0008587226,0.0003986951,0.001436639,0.001098112,0.0007433333,0.003577915],"category_scores_gemma":[0.0009524676,0.0004276995,0.0006243903,0.001087175,0.0005482145,0.0009283283,0.001567582,0.001488674,0.001411127],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009503609,"about_ca_system_score_gemma":0.0003990092,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005280003,"about_ca_topic_score_gemma":0.001509927,"domain_scores_codex":[0.9994511,0.0000866034,0.00002473753,0.0001049013,0.0002158001,0.000116867],"domain_scores_gemma":[0.9997427,0.00007525876,0.00004641764,0.00004791737,0.00001868475,0.00006908193],"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":[0.0007541163,0.0006068372,0.000639212,0.0003052984,0.0001458348,0.0004092459,0.0001138421,0.009184464,0.9214518,0.008757143,0.002374891,0.05525729],"study_design_scores_gemma":[0.0001412659,0.0009008442,0.0005060088,0.00001811337,0.00006298999,0.0002020422,0.00004049659,0.01420556,0.9710127,0.001284496,0.01157872,0.000046828],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.82549,0.005950466,0.1464988,0.0009522157,0.0002810438,0.0004682214,0.001418184,0.00306457,0.01587648],"genre_scores_gemma":[0.954743,0.003300309,0.03034694,0.0002678138,0.00004868858,0.000222914,0.001337462,0.0001560122,0.00957676],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003577915,"threshold_uncertainty_score":0.01196933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01514339634591466,"score_gpt":0.2616073116859109,"score_spread":0.2464639153399962,"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."}}