{"id":"W2994163036","doi":"10.1101/868935","title":"Fast and flexible design of novel proteins using graph neural networks","year":2019,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"graph theory and CDMA systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Fonds de recherche du Québec – Nature et technologies; Canadian Institutes of Health Research; Compute Canada; Nvidia","keywords":"Artificial neural network; Protein design; Graph; Constraint (computer-aided design); Sequence (biology); Function (biology); Variety (cybernetics); Protein sequencing","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006177269,0.000606539,0.0007660903,0.000354187,0.00009142434,0.0001313742,0.0004249452,0.0006024265,0.000008213891],"category_scores_gemma":[0.00002811336,0.0006660809,0.000152506,0.0005077368,0.0001289848,0.0001713452,0.0002364714,0.0007326884,0.000003136985],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007965182,"about_ca_system_score_gemma":0.00009100846,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003614666,"about_ca_topic_score_gemma":5.041586e-7,"domain_scores_codex":[0.9978576,0.0001177295,0.0006064426,0.0006168978,0.0002523007,0.0005489864],"domain_scores_gemma":[0.9982956,0.00006407253,0.0002772966,0.0009916223,0.0001854861,0.0001859194],"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":[0.00002308228,0.00004168248,0.001503762,0.0009147062,0.000165585,0.000005723627,0.00000997541,0.603856,0.3929538,0.0005089277,0.00001529964,0.000001565062],"study_design_scores_gemma":[0.0006487821,0.00005431385,0.00380609,0.0008803004,0.0001335037,8.804519e-8,0.000004727452,0.9045716,0.08895665,0.000003537871,0.00004789767,0.0008925003],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5634657,0.002115135,0.4314094,0.00000335393,0.001349609,0.00114436,0.00008029903,0.0004267902,0.000005400741],"genre_scores_gemma":[0.9910129,0.00008707389,0.008354439,0.00001484685,0.0002383808,0.0001042957,1.768652e-7,0.0001861824,0.000001741046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4275472,"threshold_uncertainty_score":0.999579,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02155690015186338,"score_gpt":0.2020136164678765,"score_spread":0.1804567163160131,"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."}}