{"id":"W4368374143","doi":"10.1038/s43588-023-00440-3","title":"Score-based generative modeling for de novo protein design","year":2023,"lang":"en","type":"article","venue":"Nature Computational Science","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":79,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"CIHR Skin Research Training Centre; Canadian Institutes of Health Research; Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Generative grammar; Generative model; Computer science; Protein design; Generative Design; Modular design; Image (mathematics); Protein engineering; Artificial intelligence; Protein structure; Algorithm; Biology; Programming language; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004352997,0.00008091221,0.00005450108,0.000069493,0.0002367909,0.00004187532,0.0002294167,0.0001207819,9.653514e-7],"category_scores_gemma":[0.000331052,0.00007277023,0.00003552152,0.0003741047,0.0001081944,0.00000606948,0.00004558196,0.00009425452,0.000001778207],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003138922,"about_ca_system_score_gemma":0.0006826133,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001307437,"about_ca_topic_score_gemma":0.000003267256,"domain_scores_codex":[0.9991519,0.00002042836,0.00008442735,0.0003000199,0.0002199182,0.0002232594],"domain_scores_gemma":[0.9995111,0.00002977755,0.00003179859,0.00009650982,0.0002719408,0.00005883137],"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":[0.0000297831,0.000004469158,0.00003409604,0.000004346789,0.000002605005,8.123117e-7,0.00000901547,0.7352465,0.2614865,0.002560091,0.00005042356,0.000571357],"study_design_scores_gemma":[0.0002242345,0.00007467063,0.0001996395,0.000007211625,0.000001992092,0.000003323634,0.000003320492,0.8786278,0.09748713,0.02318997,0.00008610288,0.00009455787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2861637,0.00005194496,0.7131115,0.0002673736,0.00005674114,0.0002931702,0.00001475351,0.00001787456,0.00002286146],"genre_scores_gemma":[0.7733631,6.324859e-7,0.2258832,0.000481325,0.0001052339,0.00005412052,0.0000849827,0.000006133925,0.00002133688],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4872283,"threshold_uncertainty_score":0.2967484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02197950252939696,"score_gpt":0.3049594280013972,"score_spread":0.2829799254720003,"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."}}