{"id":"W4382283964","doi":"10.1101/2023.06.25.546443","title":"PepFlow: direct conformational sampling from peptide energy landscapes through hypernetwork-conditioned diffusion","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Biomolecule; Sampling (signal processing); Computer science; Sequence (biology); Peptide; Diffusion; Generator (circuit theory); Biological system; Range (aeronautics); Chemistry; Nanotechnology; Physics; Materials science; 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":[0.001518842,0.0006622734,0.0007243587,0.0003937077,0.0003451823,0.0006152109,0.001505091,0.001300679,0.003173568],"category_scores_gemma":[0.004087249,0.0004439684,0.000473855,0.0003430535,0.0009356813,0.001124859,0.001259343,0.001609428,0.0004150655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001039073,"about_ca_system_score_gemma":0.00105161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004329641,"about_ca_topic_score_gemma":0.003812927,"domain_scores_codex":[0.9997788,0.00008972973,0.000007934552,0.00004924272,0.0000494364,0.0000247433],"domain_scores_gemma":[0.9985772,0.001019778,0.00008085689,0.0001127832,0.0001031022,0.000106331],"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":[0.00007615327,0.00003000906,0.0004287531,0.00003034309,0.00001822076,0.00005217835,0.00001758786,0.9755446,0.001685625,0.006836796,0.001123161,0.0141565],"study_design_scores_gemma":[0.000005176023,0.000002238737,0.000009428203,7.303397e-7,3.723788e-7,0.000001937332,3.563709e-7,0.998442,0.0002211697,0.001269651,0.00004603336,8.908332e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05105634,0.0002116402,0.9439311,0.0004157199,0.0000771174,0.00009035623,0.000239055,0.002475987,0.001502785],"genre_scores_gemma":[0.7393098,0.0001757987,0.2554518,0.0003584479,0.00007321571,0.0003182466,0.0006394358,0.0005815673,0.00309148],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004329641,"threshold_uncertainty_score":0.0106166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01226851295605652,"score_gpt":0.2188871849126695,"score_spread":0.206618671956613,"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."}}