{"id":"W4304014526","doi":"10.21203/rs.3.rs-1855828/v1","title":"ProteinSGM: Score-based generative modeling for de novo protein design","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Generative grammar; Computer science; Generative model; Generative Design; Artificial intelligence; Engineering; Operations management","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":[0.001903657,0.001505737,0.001342953,0.001015124,0.0007126525,0.001660477,0.003160909,0.002484969,0.01205532],"category_scores_gemma":[0.007363312,0.001329699,0.001998001,0.001392942,0.0007686485,0.001710996,0.002326828,0.003006649,0.005520412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000975462,"about_ca_system_score_gemma":0.001416012,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627587,"about_ca_topic_score_gemma":0.006212104,"domain_scores_codex":[0.9992582,0.0002881396,0.00003631651,0.0001447298,0.0002246035,0.00004787018],"domain_scores_gemma":[0.9981992,0.001043213,0.00007093407,0.000408973,0.000191931,0.00008585896],"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":[0.0003024733,0.0001267258,0.001192331,0.0003685432,0.0002596287,0.0001956508,0.000139217,0.726551,0.005543874,0.06406433,0.0286062,0.17265],"study_design_scores_gemma":[0.00002234492,0.000009846896,0.00004880733,0.000009467997,0.000009842192,0.00002263276,0.000003861498,0.9714778,0.001124635,0.0241508,0.003112856,0.000007039521],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002769902,0.0001418933,0.982024,0.0001500972,0.00005708212,0.00004480358,0.000741553,0.01296077,0.001109906],"genre_scores_gemma":[0.1590203,0.0004322734,0.8188872,0.0002746133,0.0001440869,0.0004154206,0.005276418,0.009839971,0.005709692],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01205532,"threshold_uncertainty_score":0.0403291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1003423149493608,"score_gpt":0.3912936060681858,"score_spread":0.2909512911188249,"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."}}