{"id":"W4322769977","doi":"10.3390/biom13030457","title":"Protein Design Using Physics Informed Neural Networks","year":2023,"lang":"en","type":"article","venue":"Biomolecules","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Israel Science Foundation","keywords":"Protein design; Computer science; Protein structure prediction; Robustness (evolution); Artificial neural network; Protein folding; Artificial intelligence; Sequence (biology); Machine learning; Protein structure; Physics; Chemistry","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":[0.00008336426,0.0001615204,0.0001061811,0.00003596174,0.00009707595,0.00003794379,0.0001760084,0.0001481253,0.000002530788],"category_scores_gemma":[0.00004876284,0.0001489702,0.000077226,0.0002695866,0.0001014063,0.000004892408,0.0001364304,0.00006437606,0.000009009413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001199868,"about_ca_system_score_gemma":0.00006904916,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001334258,"about_ca_topic_score_gemma":0.00000660364,"domain_scores_codex":[0.9991412,0.00004496915,0.0001449803,0.0002376639,0.0001095465,0.0003216386],"domain_scores_gemma":[0.9995472,0.000007136335,0.00006339556,0.0002716787,0.0000445821,0.00006600683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007705553,0.00001243203,0.000171412,0.00001939737,0.00004278915,0.00001781209,0.00002029351,0.02398473,0.9642763,0.0004772473,0.0006408956,0.01025963],"study_design_scores_gemma":[0.0005918235,0.0002567636,0.0003055169,0.00002467022,0.00002304116,0.00002708196,0.00004487787,0.5027463,0.4919434,0.001033287,0.002498257,0.0005050024],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5375223,0.0002784926,0.4612909,0.00005837775,0.0001514458,0.000437511,0.00001178297,0.00008610522,0.0001631158],"genre_scores_gemma":[0.9909075,0.00002905247,0.008129517,0.0001781114,0.0003574374,0.0000319855,0.000169928,0.00003345191,0.000162979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4787616,"threshold_uncertainty_score":0.6074829,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02438450743216707,"score_gpt":0.2735235489733696,"score_spread":0.2491390415412025,"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."}}