{"id":"W2048779347","doi":"10.1371/journal.pone.0025815","title":"Predicting P-Glycoprotein-Mediated Drug Transport Based On Support Vector Machine and Three-Dimensional Crystal Structure of P-glycoprotein","year":2011,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Drug Transport and Resistance Mechanisms","field":"Medicine","cited_by":115,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute of General Medical Sciences; National Institutes of Health; National Cancer Institute; European Regional Development Fund; Canadian Institutes of Health Research; European Commission","keywords":"P-glycoprotein; In silico; Support vector machine; ATP-binding cassette transporter; Docking (animal); Computational biology; Virtual screening; Homology modeling; Drug; Chemistry; Efflux; Pharmacology; Multiple drug resistance; Transporter; Bioinformatics; Biochemistry; Biology; Drug discovery; Computer science; Medicine; Machine learning; Enzyme; Gene","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":[0.0008439879,0.0006211385,0.00089126,0.00139726,0.0001607656,0.000594729,0.000418158,0.0008247807,0.000715452],"category_scores_gemma":[0.002944055,0.0001966409,0.0008455761,0.000982144,0.0001294116,0.0004878822,0.0002194291,0.0004858121,0.0003740783],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003655447,"about_ca_system_score_gemma":0.0005483896,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002667292,"about_ca_topic_score_gemma":0.001741208,"domain_scores_codex":[0.9995977,0.0001287785,0.00005048438,0.00007254528,0.0001086973,0.00004178315],"domain_scores_gemma":[0.9988447,0.0007404371,0.0001116057,0.00004515923,0.0002217408,0.00003639369],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008157896,0.0005799895,0.03995107,0.0006763307,0.0003185445,0.0008378126,0.00006966385,0.5795387,0.03796976,0.001151933,0.006036354,0.332054],"study_design_scores_gemma":[0.00001050768,0.00006519115,0.001612879,0.000005569029,0.00001463217,0.00006018603,0.000008210735,0.9944344,0.003344187,0.000172866,0.0002654139,0.00000590117],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7390082,0.003475634,0.2495321,0.0003768503,0.00008608724,0.000178672,0.002796208,0.0026743,0.001872063],"genre_scores_gemma":[0.9282214,0.0007214149,0.06724428,0.00004391335,0.00002710904,0.00009686397,0.003174435,0.00002773143,0.0004429366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002667292,"threshold_uncertainty_score":0.005303562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01839499897705222,"score_gpt":0.1922575931559396,"score_spread":0.1738625941788874,"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."}}