{"id":"W1975551833","doi":"10.1371/journal.pcbi.1002139","title":"A Computational Approach to Finding Novel Targets for Existing Drugs","year":2011,"lang":"en","type":"article","venue":"PLoS Computational Biology","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada's Michael Smith Genome Sciences Centre","funders":"Natural Sciences and Engineering Research Council of Canada; Michael Smith Health Research BC","keywords":"DrugBank; Drug; Drug discovery; Docking (animal); Computational biology; Drug repositioning; Drug target; Computer science; In silico; Approved drug; Pharmacology; Bioinformatics; Biology; Medicine; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006908103,0.0003034566,0.0003729691,0.0004358207,0.000335491,0.00009446348,0.001042364,0.0001129214,0.00001468824],"category_scores_gemma":[0.0004022755,0.0003147727,0.0001572141,0.0006384726,0.00009721119,0.0003579835,0.0004692898,0.0001665144,0.0000639784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230446,"about_ca_system_score_gemma":0.000280338,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001604527,"about_ca_topic_score_gemma":4.581183e-7,"domain_scores_codex":[0.9973853,0.0001726491,0.0005901937,0.0009530458,0.0003633349,0.0005354963],"domain_scores_gemma":[0.996731,0.002034793,0.0002262954,0.0002630857,0.0005335873,0.0002112787],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003186984,0.0003638004,0.0002190574,0.00002583887,0.00007678718,7.30715e-7,0.001998269,0.3502194,0.0001614076,0.6423496,0.0002097509,0.004343491],"study_design_scores_gemma":[0.000555658,0.0001222896,0.003264161,0.00001410869,0.000009379507,0.00002036177,0.00003734029,0.7330561,0.0001366182,0.2620823,0.0003953549,0.0003062642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0123856,0.00003720259,0.9791321,0.0003910567,0.0003917229,0.0007179059,0.0000981796,0.000255695,0.006590507],"genre_scores_gemma":[0.3666397,1.669575e-7,0.6319675,0.0008738707,0.0001106724,0.0001543284,0.0002064844,0.0000185036,0.00002871906],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3828367,"threshold_uncertainty_score":0.9999304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.169697822336543,"score_gpt":0.3451369594208517,"score_spread":0.1754391370843087,"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."}}