{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001094357,0.001470974,0.002021432,0.001294298,0.0008750357,0.0009652403,0.00195112,0.001143151,0.003456423],"category_scores_gemma":[0.001752462,0.0005394634,0.001261775,0.001641827,0.0004898658,0.0006207577,0.0008907796,0.001127805,0.0005307923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008494285,"about_ca_system_score_gemma":0.001652961,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005386376,"about_ca_topic_score_gemma":0.005670836,"domain_scores_codex":[0.9995682,0.0001917148,0.00002371959,0.00004193649,0.0001103829,0.00006408323],"domain_scores_gemma":[0.9992563,0.0004514851,0.00005785402,0.00003817242,0.0001312782,0.00006489683],"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":[0.0002831305,0.0003016721,0.001308405,0.0003676396,0.0001466224,0.0002128695,0.00004981601,0.9773428,0.002889542,0.006617905,0.001656387,0.00882323],"study_design_scores_gemma":[0.00007398285,0.0001634633,0.0002233974,0.00001557179,0.00003280935,0.00002339895,0.00002273149,0.9967548,0.000965454,0.001101923,0.0006116868,0.00001077157],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8727831,0.004359121,0.09082639,0.001475098,0.0002427977,0.0006057915,0.003116984,0.001899959,0.02469073],"genre_scores_gemma":[0.9173638,0.002403777,0.07403483,0.0003954948,0.00007332151,0.000978971,0.001936413,0.0001492928,0.002664248],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005386376,"threshold_uncertainty_score":0.01156288,"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."}}