{"id":"W3127618774","doi":"10.3389/fbinf.2022.715006","title":"Mimetic Neural Networks: A Unified Framework for Protein Design and Folding","year":2022,"lang":"en","type":"article","venue":"Frontiers in Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Mitacs; United States-Israel Binational Science Foundation","keywords":"Folding (DSP implementation); Computer science; Artificial neural network; Protein folding; Artificial intelligence; Chemistry; Engineering","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00128247,0.001133965,0.001017359,0.001111705,0.0005086353,0.001373616,0.002297176,0.001338412,0.002320136],"category_scores_gemma":[0.001839779,0.0004821052,0.0009693866,0.0009910172,0.001331877,0.001841717,0.001700631,0.001821349,0.0007977001],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001033723,"about_ca_system_score_gemma":0.001182265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001873125,"about_ca_topic_score_gemma":0.002870756,"domain_scores_codex":[0.9995151,0.0001942963,0.00002491979,0.00008108665,0.0001532161,0.0000314885],"domain_scores_gemma":[0.9996855,0.0001414153,0.00003257913,0.00005988141,0.00005588049,0.00002474267],"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":[0.00006434033,0.00003967871,0.0002602597,0.0002047983,0.00007902538,0.00008117493,0.00004443233,0.5592331,0.001866123,0.3568113,0.002835541,0.07848027],"study_design_scores_gemma":[0.000009904252,0.00002921047,0.00003661672,0.00001849634,0.000009616793,0.00002364798,0.000005871344,0.8447504,0.0005893029,0.1481132,0.006405396,0.000008362374],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001655555,0.0008668853,0.9938659,0.0002782635,0.00005797256,0.00003001863,0.00007036281,0.0003563262,0.002818717],"genre_scores_gemma":[0.1479738,0.00324914,0.8404946,0.0004139991,0.000261957,0.0005650635,0.0003818264,0.0002524466,0.006407196],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002320136,"threshold_uncertainty_score":0.007761657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008332479520323616,"score_gpt":0.2241176081079284,"score_spread":0.2157851285876048,"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."}}