{"id":"W4316114771","doi":"10.1038/s41598-023-27636-x","title":"Optimizing variant-specific therapeutic SARS-CoV-2 decoys using deep-learning-guided molecular dynamics simulations","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"SARS-CoV-2 and COVID-19 Research","field":"Medicine","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"SignalChem (Canada)","funders":"Eurostars; Karl-Franzens-Universität Graz; Austrian Science Fund; Austrian Centre of Industrial Biotechnology; Österreichische Forschungsförderungsgesellschaft; Amazon Web Services","keywords":"In silico; Molecular dynamics; Computational biology; Nicotiana benthamiana; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Coronavirus disease 2019 (COVID-19); IC50; In vitro; Chemistry; Virtual screening; Mutation; Biology; Virus; Biophysics; Cell biology; Virology; Biochemistry; Medicine; Gene","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":[0.0004080668,0.0007494568,0.0008269784,0.0002801068,0.0003215877,0.0004526806,0.0008325406,0.0008107979,0.001716811],"category_scores_gemma":[0.0009788615,0.0002995058,0.0004945207,0.0002346514,0.0004007602,0.000378844,0.0004561987,0.000717791,0.0002073484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001035683,"about_ca_system_score_gemma":0.001461079,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01009215,"about_ca_topic_score_gemma":0.01138867,"domain_scores_codex":[0.9998868,0.00002897737,0.000004266444,0.00001559092,0.0000274477,0.00003695162],"domain_scores_gemma":[0.9997069,0.0001580424,0.00002731514,0.00001689378,0.00005078777,0.00004009559],"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":[0.00009701886,0.00009579094,0.0005942752,0.00003753098,0.00003218036,0.00004712803,0.00001469184,0.9907016,0.002714263,0.001426555,0.0003943127,0.003844656],"study_design_scores_gemma":[0.00001465455,0.00002214035,0.00005168771,0.000001931616,0.000003592183,0.000002216394,0.000005458475,0.9991615,0.0003773839,0.0002324766,0.0001251118,0.00000191514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8366365,0.0008290722,0.1443827,0.0009148794,0.0001414574,0.0002098651,0.0004745417,0.0008678606,0.01554311],"genre_scores_gemma":[0.9621998,0.0002063137,0.03464021,0.0002303401,0.00001719703,0.0002166787,0.0003658144,0.0001223828,0.002001249],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01009215,"threshold_uncertainty_score":0.0200668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08221818776370086,"score_gpt":0.3771610048889202,"score_spread":0.2949428171252193,"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."}}