{"id":"W4388038481","doi":"10.21203/rs.3.rs-3501802/v1","title":"Integrated Computational Biophysics approach for Drug Discovery against Nipah Virus","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Virology and Viral Diseases","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Estadual de Campinas; University of Chittagong; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; International Foundation for CDKL5 Research; Centro Nacional de Processamento de Alto Desempenho em São Paulo; Financiadora de Estudos e Projetos; Alliance de recherche numérique du Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Binding affinities; Drug discovery; Virtual screening; Molecular dynamics; Affinities; Chemistry; Computational biology; Stereochemistry; Biophysics; Drug; Biochemistry; Biology; Computational chemistry; Pharmacology; Receptor","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006230999,0.0008481252,0.001533037,0.0009533718,0.0007497907,0.001132675,0.001330637,0.001599483,0.003971789],"category_scores_gemma":[0.0016591,0.0005070296,0.001239707,0.0008160476,0.000557857,0.0006306578,0.001043384,0.00138577,0.0004181504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009296773,"about_ca_system_score_gemma":0.002298897,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01190927,"about_ca_topic_score_gemma":0.01034978,"domain_scores_codex":[0.9997873,0.00009165213,0.000008216713,0.0000294745,0.0000490572,0.00003423314],"domain_scores_gemma":[0.9993424,0.0004586442,0.00003486156,0.00003564474,0.00008025652,0.00004812695],"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":[0.0000692813,0.00006687418,0.0009560784,0.00009661893,0.00009675467,0.0001064959,0.00002230965,0.9870855,0.0006642382,0.006419107,0.0006632152,0.003753501],"study_design_scores_gemma":[0.00002523324,0.00001685882,0.00009729593,0.000006577915,0.00001371868,0.000007394155,0.00001249629,0.9959385,0.0001376438,0.003170076,0.0005708779,0.000003331732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5367598,0.006739876,0.3870094,0.006411996,0.0007030791,0.0005104315,0.005949071,0.002187685,0.05372864],"genre_scores_gemma":[0.8668491,0.001563951,0.1218895,0.000882645,0.000150585,0.0008761796,0.002598576,0.0002069734,0.004982611],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01190927,"threshold_uncertainty_score":0.02367991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1386102595609219,"score_gpt":0.4276488170832783,"score_spread":0.2890385575223564,"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."}}