{"id":"W4406802562","doi":"10.1101/2025.01.23.634566","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":2025,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Plug-in; Docking (animal); Virtual screening; Computer science; Computational biology; Binding site; Chemistry; Drug discovery; Operating system; Biology; Biochemistry; Medicine","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":[0.001700012,0.001715808,0.001718773,0.001477334,0.0007821777,0.001606998,0.004310492,0.0009524498,0.05166803],"category_scores_gemma":[0.003163506,0.001479591,0.001439636,0.001129667,0.0005123392,0.001944969,0.002477331,0.002814786,0.0217513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000899252,"about_ca_system_score_gemma":0.001907277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005643236,"about_ca_topic_score_gemma":0.005132901,"domain_scores_codex":[0.99896,0.0001389002,0.00005148107,0.0001409514,0.0005903238,0.0001183248],"domain_scores_gemma":[0.9992379,0.0003036811,0.00005195251,0.0001504506,0.0001656952,0.00009033043],"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":[0.0007036485,0.0001502288,0.001408606,0.001311748,0.0002396751,0.000421495,0.0001667311,0.0320725,0.02286699,0.01837801,0.7586148,0.1636655],"study_design_scores_gemma":[0.0006257527,0.0001514049,0.002531252,0.000224077,0.00008155041,0.0006131091,0.00006028745,0.4121814,0.07174932,0.02724874,0.4841056,0.0004274034],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.01329544,0.001222302,0.5122594,0.0008412541,0.000499654,0.0002731816,0.03309411,0.4288675,0.00964711],"genre_scores_gemma":[0.135934,0.002825329,0.5718763,0.000919198,0.000204193,0.001885931,0.09472135,0.1631215,0.02851215],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.05166803,"threshold_uncertainty_score":0.1728467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01273141357948442,"score_gpt":0.2740575597407469,"score_spread":0.2613261461612624,"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."}}