{"id":"W4411332264","doi":"10.1093/bioadv/vbaf129","title":"NRGSuite-Qt: a PyMOL plugin for high-throughput virtual screening, molecular docking, normal-mode analysis, the study of molecular interactions, and the detection of binding-site similarities","year":2024,"lang":"en","type":"article","venue":"Bioinformatics Advances","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Plug-in; Docking (animal); Virtual screening; Computational biology; Computer science; Binding site; Chemistry; Bioinformatics; Drug discovery; Biology; Operating system; Medicine; Biochemistry","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003226819,0.0001911159,0.0002834759,0.0001567537,0.000146363,0.00007811451,0.0002221067,0.0000755494,0.000001794774],"category_scores_gemma":[0.00009623541,0.0001198376,0.0001809794,0.0003263789,0.0002334165,0.00004425463,0.0001540107,0.0001229669,3.15177e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009470329,"about_ca_system_score_gemma":0.00003038731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000968206,"about_ca_topic_score_gemma":0.0003379088,"domain_scores_codex":[0.9988542,0.00006065581,0.0005071368,0.0001901845,0.0002149771,0.0001728582],"domain_scores_gemma":[0.9991158,0.00009690058,0.0002773546,0.0003648559,0.0001168782,0.00002826559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003351793,0.0004324195,0.002141946,0.002254214,0.01465015,0.00002037846,0.01700749,0.2403264,0.5179877,0.02522134,0.0001967904,0.1764094],"study_design_scores_gemma":[0.004261841,0.002631373,0.0004245494,0.0001973932,0.00358696,0.00005682236,0.01208373,0.3756426,0.5811713,0.002264345,0.01686592,0.0008131505],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6825826,0.004454452,0.3119111,0.00005765715,0.0001296168,0.0006468373,0.0001498845,0.00001296701,0.00005484813],"genre_scores_gemma":[0.996072,0.0004507382,0.003115213,0.00007184019,0.00004127001,0.00009025306,0.0001147153,0.00001550033,0.00002846826],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3134894,"threshold_uncertainty_score":0.4886837,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004588894427499391,"score_gpt":0.2638198506195027,"score_spread":0.2592309561920033,"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."}}