{"id":"W2118518847","doi":"10.1038/srep00571","title":"Finding low-energy conformations of lattice protein models by quantum annealing","year":2012,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Quantum Computing Algorithms and Architecture","field":"Computer Science","cited_by":387,"is_retracted":false,"has_abstract":true,"ca_institutions":"D-Wave Systems (Canada)","funders":"Division of Chemistry; National Science Foundation","keywords":"Quantum annealing; Quantum; Protein structure prediction; Protein folding; Statistical physics; Simulated annealing; Lattice (music); Computer science; Energy landscape; Physics; Protein structure; Biological system; Algorithm; Quantum mechanics; Quantum computer; Thermodynamics; Biology","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":[0.001019522,0.0003200822,0.00064421,0.0004727699,0.0006736668,0.001077196,0.000910619,0.0009135773,0.001259238],"category_scores_gemma":[0.004045683,0.0005242027,0.0005195465,0.0004233218,0.001454914,0.001320741,0.0006880092,0.001032538,0.0001614842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001430479,"about_ca_system_score_gemma":0.0008396427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003832682,"about_ca_topic_score_gemma":0.003460434,"domain_scores_codex":[0.9995763,0.0002046464,0.00001924316,0.00004860361,0.0001051681,0.00004611015],"domain_scores_gemma":[0.9985375,0.0009859214,0.00008043101,0.0002187395,0.0001057772,0.00007154132],"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":[0.00008420184,0.00007791536,0.0008495083,0.00002872303,0.00002059547,0.0000326688,0.00008024732,0.9566322,0.003518828,0.03188922,0.0003065347,0.00647936],"study_design_scores_gemma":[0.00001234963,0.00001351282,0.00006149454,0.00000174636,0.000001871317,0.000002131221,0.000007514329,0.988223,0.000572438,0.01101275,0.00008835996,0.000002863957],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7535043,0.000223696,0.2404197,0.0005141001,0.00003338177,0.00006700654,0.00006324492,0.000392686,0.004781837],"genre_scores_gemma":[0.9032266,0.00008659725,0.09576823,0.00004296706,0.000007429762,0.00009068254,0.00009308444,0.00005103351,0.000633541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003832682,"threshold_uncertainty_score":0.0103789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01621352875341884,"score_gpt":0.2366885133571532,"score_spread":0.2204749846037343,"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."}}