{"id":"W2561790043","doi":"10.1016/j.bbrc.2016.12.115","title":"Computational proteome-wide screening predicts neurotoxic drug-protein interactome for the investigational analgesic BIA 10-2474","year":2016,"lang":"en","type":"article","venue":"Biochemical and Biophysical Research Communications","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Institute of General Medical Sciences; University of Toronto","keywords":"Neurotoxicity; Proteome; Drug discovery; Medicine; Pharmacology; Drug; In silico; Interactome; Bioinformatics; Proteomics; Computational biology; Toxicity; Biology; Internal medicine; Biochemistry; Gene","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":[0.001407415,0.0001850824,0.0001815699,0.0001802699,0.0008198542,0.0002950432,0.002650857,0.00006437003,0.00001528038],"category_scores_gemma":[0.002799191,0.0001139353,0.000123513,0.0009504345,0.001436497,0.0004623202,0.001837938,0.0003938468,0.00003480949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007457427,"about_ca_system_score_gemma":0.0002821809,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000165107,"about_ca_topic_score_gemma":0.000002362347,"domain_scores_codex":[0.9970509,0.0006944137,0.0003727785,0.0005416019,0.0008988105,0.0004415315],"domain_scores_gemma":[0.983527,0.01421805,0.0001033757,0.001157311,0.0007235596,0.0002707252],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001810102,0.0007834587,0.0002655505,0.0001063927,0.0001711777,0.000001991528,0.0004160025,0.0003892465,0.4904075,0.4105287,0.01539321,0.08135571],"study_design_scores_gemma":[0.001393231,0.0002579326,0.02144695,0.0004446776,0.00001847601,0.000007270492,0.0000468839,0.6103311,0.05284712,0.298048,0.01468334,0.0004750404],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2531272,0.0004125507,0.6372668,0.1064612,0.00005117696,0.002245012,0.000116271,0.0001627685,0.0001569594],"genre_scores_gemma":[0.8892438,0.00002796272,0.1091165,0.0002574524,0.0001070223,0.000898962,0.00004642539,0.00001508496,0.0002867468],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6361166,"threshold_uncertainty_score":0.6305739,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1258789343948589,"score_gpt":0.3918597547591103,"score_spread":0.2659808203642514,"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."}}