{"id":"W4328048996","doi":"10.1021/acs.jcim.2c01504","title":"Vina-GPU 2.0: Further Accelerating AutoDock Vina and Its Derivatives with Graphics Processing Units","year":2023,"lang":"en","type":"article","venue":"Journal of Chemical Information and Modeling","topic":"Computational Drug Discovery Methods","field":"Computer Science","cited_by":121,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Jiangsu Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Virtual screening; Graphics; General-purpose computing on graphics processing units; Parallel computing; CUDA; Computer graphics (images); Computational science; Drug discovery; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005204291,0.00009392444,0.0001383401,0.0002096868,0.0000926775,0.0003157635,0.000154967,0.00003999838,7.116174e-7],"category_scores_gemma":[0.0001841846,0.00007221876,0.00001856977,0.0005720844,0.00002061827,0.003613167,0.0001143129,0.0001925662,7.120723e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001675443,"about_ca_system_score_gemma":0.0001352978,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.276256e-7,"about_ca_topic_score_gemma":9.804861e-8,"domain_scores_codex":[0.9990699,0.00003049462,0.000403196,0.00007390187,0.0002957377,0.0001267869],"domain_scores_gemma":[0.9989854,0.000113047,0.0002673325,0.0000489857,0.0004979591,0.0000873003],"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":[0.0001034713,0.00003311025,0.0002081569,0.0004189344,0.00007319688,0.00001047352,0.02909252,0.7265992,0.01134803,0.0242208,0.0001049527,0.2077872],"study_design_scores_gemma":[0.000308066,0.00003196736,0.00009692888,0.0001396307,0.000004772938,0.00008858192,0.0003410192,0.9936331,0.002588534,0.002608638,0.00006382156,0.00009496209],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5569931,0.00008113904,0.4421984,0.0005792939,0.00001995595,0.00003252198,4.024112e-7,0.00002543643,0.00006974967],"genre_scores_gemma":[0.9573328,0.00005346114,0.04231793,0.0002520986,0.00003399953,0.000001337627,0.000001990677,0.00000422984,0.000002101416],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4003398,"threshold_uncertainty_score":0.3044915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07210951418829442,"score_gpt":0.3146073911673918,"score_spread":0.2424978769790974,"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."}}