{"id":"W2990085785","doi":"10.1007/s10822-019-00253-5","title":"Prediction of P-glycoprotein inhibitors with machine learning classification models and 3D-RISM-KH theory based solvation energy descriptors","year":2019,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"","keywords":"Solvation; In silico; Artificial intelligence; Molecular descriptor; Quantitative structure–activity relationship; Robustness (evolution); Test set; Machine learning; Support vector machine; Computer science; Chemistry; Pattern recognition (psychology); Biological system; Computational chemistry; Molecule; Biology; Biochemistry; 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":[0.000425336,0.0003932077,0.0007516597,0.0008874362,0.0001783099,0.0007395303,0.0004723427,0.0005139641,0.001141274],"category_scores_gemma":[0.001067586,0.0001314231,0.001023209,0.0004933352,0.0001470523,0.0004202857,0.0002840566,0.0005166693,0.0003435546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004777657,"about_ca_system_score_gemma":0.0006512971,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002670211,"about_ca_topic_score_gemma":0.002102185,"domain_scores_codex":[0.9998597,0.00004047259,0.00001321996,0.00002385541,0.00004066922,0.00002204082],"domain_scores_gemma":[0.9996487,0.0002132762,0.00005085537,0.00001450318,0.00005681324,0.00001577848],"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":[0.0009167398,0.0004424091,0.01435119,0.0002631542,0.0002767881,0.0002800922,0.0000202962,0.8645878,0.00786932,0.003303551,0.00375739,0.1039312],"study_design_scores_gemma":[0.00001639465,0.00004911137,0.0005051976,0.000003955423,0.00001972804,0.0000153594,0.000003176763,0.9980289,0.0008096841,0.0004274386,0.0001173726,0.000003514204],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8557941,0.004153556,0.1324261,0.0007314638,0.0001173043,0.0001418743,0.001660337,0.001035846,0.003939305],"genre_scores_gemma":[0.9873393,0.0003814193,0.01066791,0.00007525074,0.00002580368,0.00005278685,0.0008315834,0.00002005974,0.0006059018],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.002670211,"threshold_uncertainty_score":0.005309343,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02841033867935707,"score_gpt":0.2251861375840109,"score_spread":0.1967757989046538,"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."}}