{"id":"W3118853958","doi":"10.1007/s10822-020-00364-4","title":"Predicting PAMPA permeability using the 3D-RISM-KH theory: are we there yet?","year":2021,"lang":"en","type":"article","venue":"Journal of Computer-Aided Molecular Design","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Institute for Nanotechnology; University of Alberta","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"In silico; Permeability (electromagnetism); Membrane permeability; Chemistry; Experimental data; Membrane; Biological system; Mathematics; Biochemistry; Biology","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":[0.004726815,0.0003418048,0.0005623523,0.0001747176,0.0003167011,0.000640431,0.001707527,0.0001145982,0.00001903377],"category_scores_gemma":[0.0005837641,0.0002562408,0.0004688214,0.0008370265,0.0001238984,0.0007159315,0.0006652751,0.0006973821,0.000003794655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000198114,"about_ca_system_score_gemma":0.0009662652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003602147,"about_ca_topic_score_gemma":7.854786e-7,"domain_scores_codex":[0.9922382,0.004744363,0.000937794,0.000511763,0.001122833,0.0004451031],"domain_scores_gemma":[0.9942169,0.00261015,0.001022349,0.0009639114,0.0009848599,0.0002018399],"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":[0.0000566959,0.0002305486,0.0004028661,0.00004653274,0.0003047309,0.001349975,0.001233668,0.935973,0.009910066,0.01181559,0.0002549877,0.0384213],"study_design_scores_gemma":[0.0006649802,0.000186197,0.001527279,0.0003698183,0.00009745865,0.001861606,0.0001891559,0.884715,0.01631224,0.09334749,0.0004078003,0.0003209403],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1153245,0.002142732,0.8788771,0.002151533,0.001172411,0.0002137929,0.000002237094,0.00005104623,0.00006464082],"genre_scores_gemma":[0.432457,0.00003998174,0.5664865,0.0006780457,0.0002938817,0.000002602798,5.741905e-7,0.00002835775,0.00001314635],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3171325,"threshold_uncertainty_score":0.999989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05448538229946748,"score_gpt":0.311373341279426,"score_spread":0.2568879589799585,"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."}}