{"id":"W7117551302","doi":"10.1038/s41467-025-67808-z","title":"A multi-grained symmetric differential equation model for learning protein-ligand binding dynamics","year":2025,"lang":"en","type":"article","venue":"Nature Communications","topic":"Protein Structure and Dynamics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Toronto","funders":"","keywords":"Solver; Ordinary differential equation; Symmetry (geometry); Dynamics (music); Key (lock); Differential equation","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":[0.0005472424,0.0004303911,0.0006534449,0.0003392699,0.0004673363,0.0006491242,0.001464572,0.001467273,0.002400738],"category_scores_gemma":[0.001790988,0.0003395947,0.00056747,0.0004303248,0.0007759949,0.000857209,0.0009652026,0.001116331,0.0004330076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009995759,"about_ca_system_score_gemma":0.002131451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009388381,"about_ca_topic_score_gemma":0.008261458,"domain_scores_codex":[0.9998091,0.00005411773,0.000009825024,0.00003040322,0.0000735721,0.00002312022],"domain_scores_gemma":[0.9995393,0.0002147359,0.00005625217,0.00004609239,0.0000857108,0.00005806394],"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":[0.00002664907,0.0000274282,0.0004133471,0.00003062799,0.00001136393,0.00004460497,0.00001719599,0.9755118,0.001414915,0.0166409,0.0007200003,0.005141046],"study_design_scores_gemma":[0.00000239347,0.000002929097,0.00001931323,0.000001026428,6.342318e-7,0.000002034988,8.783186e-7,0.998759,0.00008169314,0.0009236617,0.000205198,0.000001120045],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06924605,0.0005141431,0.91752,0.001098378,0.0001844221,0.0001073058,0.0006035158,0.0004819455,0.01024422],"genre_scores_gemma":[0.7356998,0.0005832846,0.2497678,0.0005096602,0.00009663398,0.0004761522,0.0009973452,0.0002585889,0.01161072],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009388381,"threshold_uncertainty_score":0.01866746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01840818038500339,"score_gpt":0.3048954418211672,"score_spread":0.2864872614361638,"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."}}