{"id":"W4406213027","doi":"10.1093/bioinformatics/btaf010","title":"FlowPacker: protein side-chain packing with torsional flow matching","year":2025,"lang":"en","type":"article","venue":"Bioinformatics","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Canadian Institutes of Health Research; Alliance de recherche numérique du Canada","keywords":"Computer science; Side chain; Matching (statistics); Graph; Algorithm; Source code; Folding (DSP implementation); Code (set theory); Chain (unit); Sequence (biology); Flow (mathematics); Set (abstract data type); Theoretical computer science; Programming language; Chemistry; Mathematics; Engineering; Biochemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001325282,0.001862566,0.001265584,0.001069042,0.0007827057,0.001334017,0.003808432,0.001723189,0.01523951],"category_scores_gemma":[0.004947237,0.000953952,0.001298725,0.0009728924,0.0008981579,0.002642548,0.001723786,0.00255266,0.005330655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009678698,"about_ca_system_score_gemma":0.002272442,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009463669,"about_ca_topic_score_gemma":0.007177755,"domain_scores_codex":[0.9995998,0.00006206749,0.00001863729,0.0001301815,0.000139774,0.00004970559],"domain_scores_gemma":[0.9991978,0.000362268,0.00008585589,0.00014659,0.0001236135,0.00008381012],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001110452,0.000619326,0.006819583,0.001496205,0.000429589,0.0004489038,0.0003757057,0.3660524,0.02844278,0.03051055,0.174033,0.3896615],"study_design_scores_gemma":[0.0001046647,0.00005435461,0.0003315881,0.00002335287,0.00001591621,0.00007296997,0.00001073669,0.973678,0.007076942,0.01040766,0.00819712,0.00002670076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03978483,0.00147464,0.7739261,0.0007312957,0.0004177258,0.0005245425,0.006859014,0.1706858,0.005596141],"genre_scores_gemma":[0.2338213,0.001064659,0.7147487,0.0008764953,0.0002899418,0.0008540908,0.01988913,0.01890643,0.009549168],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01523951,"threshold_uncertainty_score":0.05098122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003672734255921913,"score_gpt":0.2132390287635326,"score_spread":0.2095662945076107,"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."}}