{"id":"W3110338636","doi":"10.3390/toxins12120746","title":"Ligand-Based Virtual Screening, Molecular Docking, Molecular Dynamics, and MM-PBSA Calculations towards the Identification of Potential Novel Ricin Inhibitors","year":2020,"lang":"en","type":"article","venue":"Toxins","topic":"Toxin Mechanisms and Immunotoxins","field":"Immunology and Microbiology","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Institut National de la Recherche Scientifique","funders":"Univerzita Hradec Králové; Fundação de Amparo à Pesquisa e Inovação do Espírito Santo; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Ministerstvo Zdravotnictví Ceské Republiky; Universidade Federal de Lavras; Fakultní nemocnice Hradec Králové; Univerzita Karlova v Praze","keywords":"PubChem; Ricin; Virtual screening; Docking (animal); Chemistry; Molecular dynamics; In silico; Active site; Small molecule; Computational biology; Stereochemistry; Biochemistry; Toxin; Computational chemistry; Biology; Enzyme; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002674102,0.0002333014,0.0002870229,0.0001007587,0.0002520181,0.00004318039,0.0003151432,0.0002422954,0.00007645897],"category_scores_gemma":[0.0001525653,0.0001964318,0.0001689485,0.0002689984,0.0002711777,0.00009094118,0.0001447898,0.0003257278,0.00002858565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003731304,"about_ca_system_score_gemma":0.0001066668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001924294,"about_ca_topic_score_gemma":0.00001794381,"domain_scores_codex":[0.9985142,0.0001256976,0.0005705422,0.0003830118,0.0001129291,0.0002935598],"domain_scores_gemma":[0.9991592,0.00003534115,0.000291566,0.0003645237,0.0001047588,0.00004457248],"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":[0.00005952465,0.00009431247,0.00006950911,0.0000131981,0.0001251753,0.000005204319,0.0004566023,0.003853772,0.9818857,0.009890541,0.0001015589,0.003444912],"study_design_scores_gemma":[0.001395066,0.0002757037,0.00247302,0.00002737648,0.0001419907,0.00001369289,0.0004209894,0.01954931,0.9742225,0.0000804212,0.001155555,0.0002444101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6998106,0.0006394878,0.2965023,0.002085075,0.000272107,0.0003418858,0.0002044329,0.00006416919,0.00007992835],"genre_scores_gemma":[0.9983706,0.00001238795,0.00048338,0.0006301618,0.00003046323,0.00001874129,0.0002560447,0.00003328696,0.0001648917],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2985601,"threshold_uncertainty_score":0.8010255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01165043022642407,"score_gpt":0.2262067749474712,"score_spread":0.2145563447210471,"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."}}