{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001996538,0.002454257,0.001734912,0.00174853,0.0008113416,0.001733704,0.005187458,0.001204192,0.04208812],"category_scores_gemma":[0.003866293,0.001817592,0.001863397,0.001059238,0.0006194515,0.002543012,0.003667305,0.00371816,0.02436071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008733486,"about_ca_system_score_gemma":0.002072697,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003875336,"about_ca_topic_score_gemma":0.004316994,"domain_scores_codex":[0.9987983,0.000166852,0.00007302709,0.0002087933,0.0005868322,0.0001662432],"domain_scores_gemma":[0.9990135,0.0003694047,0.00008706754,0.000210946,0.0001917949,0.0001273415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008702909,0.0002309325,0.002220074,0.001919918,0.0004377612,0.0005294174,0.0002483384,0.01415756,0.0311556,0.01122133,0.7512476,0.1857611],"study_design_scores_gemma":[0.0007965969,0.0002873954,0.004203495,0.0003607134,0.0001553048,0.001366079,0.00007308227,0.2559774,0.1153637,0.02098237,0.5997424,0.0006916149],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"software","genre_gemma":"software","genre_scores_codex":[0.006955213,0.0009574821,0.3544979,0.0004844908,0.0004254535,0.000243029,0.01968462,0.6121517,0.004600222],"genre_scores_gemma":[0.09037898,0.002650056,0.5574452,0.001238539,0.0002162148,0.002376947,0.1012828,0.2230309,0.02138034],"genre_candidate":"software","genre_consensus":"software","teacher_disagreement_score":0.04208812,"threshold_uncertainty_score":0.1407988,"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."}}